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National Institute of Mental Health, (NIMH), Pilot Explorations of Neurofeedback issues in ADHD, using SmartBrain Technologies, (September, 2008).
We are excited to announce that NIMH, (National Institute of Mental Health), has funded an ADHD research study using the SmartBrain Technology. The principal investigator, L. Eugene Arnold, M.D., M.Ed., Professor Emeritus of Psychiatry, at Ohio State University is an internationally renowned researcher in the treatments of ADHD and will be heading up this study. This study is planned to begin in the fall of 2008.
A Randomized, Double-Blind Clinical Trial of EEG Neurofeedback Treatment for Attention Deficit / Hyperactivity Disorder (ADHD), (2002), A Double Blind, Randomized, Sham Treatment Study for ADHD boys aged 9-11, funded by Johnson and Johnson Consumer Products Companies, Inc., has just been completed, but is not yet published. This research was presented at the ISNR Conference, 2005.
Parents were blinded to study group assignment.
Outcome measures included independent, blinded evaluations by two consulting physicians to establish baseline diagnoses and to assess clinical improvement over the course of treatment for each individual patient.
Clinic staff was not be blinded to study group assignment.
The Contact Control Group, (19 subjects) made the same number of visits to the same clinic as the Experimental Group, (17 subjects) and they took part in an equal number of computer-based sessions and contact hours in an identical room with the same type of equipment. The only difference between the groups was that the Experimental subjects participated in computer-based EEG Biofeedback during their in-clinic sessions while the Control subjects did not. During their in-clinic sessions the Control subjects played a computer game lacking any neurophysiologically contingent feedback signal.
Contact Control Group experienced a 47% study drop out rate.
Experimental Group experienced a 6% study drop out rate.
No significant effects were noted on post study outcome measures for the Contact Control group.
Significant effects (P=<.025) were noted on the post Parent Conners, and post CGI (P=<.001) by both independent blinded physicians.
Child and Adolescent Psychiatric Clinics of North America, Volume 14, (2005).
A comprehensive review of the research on Neurofeedback has been published in a special issue of devoted to Emerging Interventions. In theintroductory chapter, the volume editors assess the degree of scientific support for Neurofeedback using standards developed by the child psychiatry professional organization (AACAP). They conclude that Neurofeedback uniformly shows significant benefit for 70 to 80% of participants, with an effect size for Neurofeedback equivalent to that of stimulants, as measured by computerized tests of attention and impulsivity (continuous performance tests) as well as standardized behavioral rating scales.
Forty-Six patients (aged 8–21 years) diagnosed with ADHD participated in the study. Two groups of 23 patients received the treatment of their (or a parent’s) choice (either stimulant medication or 20 sessions of EEG Biofeedback). None of the participants in either group discontinued treatment during the study.
The results of this study indicated significant improvements (P < 0.05) on the TOVA and several subscales of the Behavioral Assessment System for Children (e.g., hyperactivity, attention problems, and externalizing behaviors).
Comparison with a bona fide treatment for ADHD, (stimulant medication) revealed no difference in the effects of these treatments after 20 sessions.
Similarly, there was no significant difference in the percentage of patients who demonstrated clinical improvement with EEG Biofeedback (83%) and stimulant medication (87%).
Each group was composed of 9 children, 6 of whom were diagnosed with ADHD and 3 of whom were diagnosed with ADHD in combination with a learning disorder.
Patients who received EEG Biofeedback participated in 40 training sessions, (45 minutes each). None of the participants discontinued treatment during the study.
The results of the study revealed that children treated with EEG Biofeedback demonstrated significant increases ( P < 0.05) on measures of intelligence and a reduction in symptoms of inattention on a behavioral rating scale. No adverse effects were reported.
All participants received the following treatments: stimulant medication (dosage titrated based on the results of behavioral measures and the TOVA), a 10-week parenting program with follow-up individualized parental counseling provided as needed, and academic support at school (provided via an individual education plan or 504 accommodation plan).
All patients were given the opportunity to receive EEG Biofeedback as part of their treatment program. Fifty-one families chose to include EEG Biofeedback in their child’s treatment. Forty-nine did not.
All children were treated with stimulant medication, (Ritalin). The average dose of Ritalin administered to the patients of both groups was 25 mg., (typically 10 mg. after breakfast, 10 mg. at lunchtime, and 5 mg. after school). The range of dosages was 15 to 45 mg. per day for both groups.
The beneficial effects of stimulant medication and EEG Biofeedback indicated that parenting style was a moderating factor in both treatments. The EEG Biofeedback and the non-EEG Biofeedback groups demonstrated significant improvements on behavioral ratings and the TOVA when tested while using medication.
After the medication wash-out period, relapse was noted on behavioral and CPT measures in each of the participants who had not received EEG Biofeedback. Sustained improvement on the QEEG measure was not evident on the QEEG measure once stimulant medication was discontinued in patients who had not received EEG Biofeedback.
There was no indication that the use of stimulant medication yielded any enduring benefits after 3 years (total) of pharmacologic treatment. Although patients who had never been treated with EEG Biofeedback continued to demonstrate positive response on behavioral ratings, the TOVA, and the QEEG when tested with medication, relapse occurred in each of these participants when tested without medication 12, 18, 24, and 36 months after initial evaluation and treatment.
Patients whose treatment included EEG Biofeedback continued to demonstrate significantly improved levels of cortical activation on the QEEG measure and sustained gains on the TOVA and behavioral ratings throughout the 3-year period, even when medications were withdrawn.
Thirty-four (80%) of the patients whose treatment included EEG Biofeedback were able to decrease daily dosage of stimulant by at least 50%. By contrast, none of the patients who did not receive EEG Biofeedback was able to reduce dosage (85% increased dose).
Two EEG Biofeedback protocols were used. Children diagnosed with an inattentive type of ADHD received training designed to increase production of beta 1 activity (and reduce theta) at C3. Children with the hyperactive or impulsive subtype participated in training intended to increase production of SMR and reduce beta 2 (22–30 Hz) at C4. Patients with the combined type of ADHD received both kinds of training. Sessions were conducted three times per week (30–60 minutes) for a 12-week period.
Pretreatment measures included a test of intelligence [WISC-R), computerized tests of attention (TOVA) and behavioral rating scales.
Statistical analysis of pretreatment measures indicated that the groups were comparable before treatment. Post treatment analysis indicated that EEG Biofeedback and Ritalin were associated with significant improvements on computerized tests of attention and behavioral rating scales. The degree of clinical gains noted after EEG Biofeedback was comparable to that associated with stimulant medication. No adverse effects were reported.
75% of patients treated with EEG Biofeedback in controlled group studies responded positively when the treatment was provided in an open trial in which patient choice determined type of treatment (e.g., stimulant medication, EEG Biofeedback, parental counseling).
Follow-up studies also have provided evidence that unlike medication effects (which quickly dissipate when treatment is discontinued), EEG Biofeedback seems to exert a far more enduring effect on the core symptoms of ADHD and associated functional problems.
Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H., & Kaiser, J. (2003). Neurofeedback treatment for attention deficit/hyperactivity disorder in children: A comparison with methylphenidate. Applied Psychophysiology and Biofeedback, 28, 1-12.
Linden, M., Habib, T., & Radojevic, V. (1996). A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback & Self-Regulation, 21(1), 35-49.
Monastra, V. J., (2005). Electroencephalographic biofeedback (neurotherapy) as a treatment for attention deficit hyperactivity disorder: Rationale and empirical foundation. Child & Adolescent Psychiatric Clinics of North America, 14(1), 55-82.
Monastra, V. J., Lynn, S., Linden, M., Lubar, J. F., Gruzelier, J., & LaVaque, T. J. (2005). Electroencephalographic biofeedback in the treatment of attention-deficit/hyperactivity disorder. Applied Psychophysiology & Biofeedback, 30(2), 95-114.
Monastra, V. J., Monastra, D. M., & George, S. (2002). The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Applied Psychophysiology & Biofeedback, 27(4), 231-249.
Beauregard, M & Levesque, J, (2006). Functional magnetic resonance imaging investigation of the effects of EEG Neurfeedback training on the neural bases of selective attention and response inhibition in children with attention-deficit/hyperactivity disorder.
This study showed that Neurofeedback with children with ADHD resulted in normalization of brain activation levels in the frontal circuits in the brain that are involved in attention. As shown in pre- and post-Neurofeedback fMRI studies, blood oxygenation levels of 24 ADHD participants normalized with Neurofeedback training, with increased blood oxygenation in the prefrontal cortex bilaterally, and in the anterior portion of the cingulate gyrus. No such changes were seen in non-treatment controls.
In addition to obtaining behavioral and neuropsychological measures, functional magnetic resonance imaging (fMRI) was conducted on each participant pre- and post-treatment during completion of the Counting Stroop Task and the Go/No Go Task.
As anticipated, children who received EEG Biofeedback demonstrated significant improvement on behavioral and neuropsychological tests of attention.
The children who had received EEG Biofeedback demonstrated significant activation of the right anterior cingulated cortex, the left caudate nucleus, and in the lateral prefrontal cortex (bilaterally) in comparison to pretreatment findings. No such change in activation was noted in the control group.
Berkley Medical Journal, Issues 2002, by Gordon Kwan. Play Attention! Can custom-made video games help kids with Attention Deficit Disorder.
For children with Attention Deficit Hyperactivity Disorder (ADHD), life can feel like a never-ending video game. They are wired–restless, impulsive, and easily distracted. Their minds are constantly bombarded with different elements of reality that compete for their attention.
So far, the most popular treatment for ADHD has been Ritalin, a rapid-acting stimulant for adults that has the opposite effect in children, calming the jitters associated with the disorder. According to the National Institute of Mental Health, about three percent of American school children take stimulants like Ritalin regularly. However current research suggests a surprising new strategy for treating this disorder: video games linked to brain-wave biofeedback that can help kids with ADHD train their minds to tune in and settle down.
It is difficult for a child with ADHD to learn how to self-regulate and know what it feels like to concentrate. Biofeedback teaches patients to control normally involuntary body functions such as heart rate by providing real-time monitoring of such responses. More than 15 years of studies show that with the aid of a computer display and an EEG sensor attached to the scalp, ADHD patients can learn to modulate brain waves associated with focusing. Increasing the strength of high-frequency beta waves and decreasing the strength of low-frequency theta waves, for example, creates a more attentive state of mind. With enough training, changes become automatic and lead to improvements in grades, sociability, and organizational skills.
Despite its proven success, the technique has not become a mainstream treatment for several good reasons. First, unlike drug therapy, which can have immediate results, a typical course of biofeedback treatment takes a series of about 40 one-hour sessions over a span of several months before benefits become apparent. Second, it is more expensive than drugs. Costs range from $3,000 to $4,000 for these treatments, so insurance companies tend to pick the less expensive option. Finally, biofeedback training requires the very kind of prolonged concentration that patients with ADHD struggle to attain.
Alan Pope, a behavioral scientist at NASA Langley Research Center in Hampton, Virginia, came up with a more engaging approach through work with NASA flight simulators. He was determining the degree of interaction with cockpit controls necessary to help pilots stay attentive during routine flights. In an experiment, he linked the level of automation in the cockpit to the pilots’ brain-wave signals, so that some controls switched from autopilot to manual when the pilot started to lose focus. He found that with practice the pilots could begin to adjust the controls to the level of automation that felt most comfortable by regulating their own brain waves.
Pope applied his findings to help ADHD patients stay focused by rewarding an attentive state of mind. He realized, however, that the simple displays that were already part of biofeedback treatment may not be enough to hold the interest of restless youngsters. He then chose several common video games and linked the biofeedback signal from the player’s brain waves to the handheld controller that guides the games’ actions. “In one auto-racing game, a car’s maximum speed increases if the player’s ratio of beta to theta waves improves. The same sort of feedback also controls the steering,” Pope says.
In the test, six Sony PlayStation games were used with 22 boys and girls between the ages of nine and thirteen who had ADHD. Half the group received traditional biofeedback training; the other half played the modified video games. After 40 one-hour sessions, both groups showed substantial improvements in everyday brain-wave patterns as well as in tests of measuring attention span, impulsiveness, and hyperactivity. Parents in both groups also reported that their children were doing better in school.
The difference between the two groups was motivation. “In the video-game group, there were fewer no-shows and no dropouts,” according to Pope. The parents were more satisfied with the results of the training, and the kids seemed to have more fun.
Since children are more motivated toward video-game biofeedback and may already be familiar with video games, they will not need one-on-one coaching to master the technique. As a result, the cost of the treatment should be reduced and maybe even permit “do-it-yourself” biofeedback. One North Carolina company markets their system as a fun bike helmet and game-like video exercises that work on almost any computer. The helmet is lined with sensors that monitor the child’s brain waves, and the child actually controls the computer video exercises by mind alone. Parents should not expect regular video games to help their children. The wrong kinds of video games might actually hurt children with attention disorders.
Brain-wave biofeedback alone may not be a substitute for drug therapy. Professor Stephen Hinshaw, an expert in the field of child clinical psychology at UC Berkeley, gives a reserved opinion about biofeedback treatment. “Biofeedback is a promising potential alternative, but unfortunately the kinds of really well-controlled studies that might support its clinical benefits have yet to be performed.” The two treatments have complementary aspects that make them effective as adjuncts. A dose of Ritalin, for example, acts quickly but only for a few hours, and most patients take it only on school days. Brain-wave regulation takes a long time to learn but has the potential for longer-lasting effects.
Researchers and clinicians are realizing that ADHD is not easily outgrown. Most doctors support an approach that combines good nutrition, sleep, exercise, and learning strategies as well as biofeedback and drug therapy. The possibilities for brain-wave biofeedback are very promising since its benefits could last a lifetime. Video game biofeedback therapy may provide a more tolerable and long-lasting form of treatment for children through a medium they are more likely to enjoy.
Effectiveness of Video Game EEG Neurofeedback on ADHD. (2000). NASA and Eastern Virginia Medical School conducted a research study to determine the effectiveness of video game EEG Neurofeedback on ADHD. In one research group kids received standard EEG Neurofeedback for the treatment of ADHD. In the other research group, kids played brain wave enabled video games. Both the video game and standard EEG Neurofeedback groups improved significantly on most main ADHD outcome measures. No significant difference in treatment change was seen in group comparisons. Parents’ appraisal of treatment effect on ADHD was more positive for the video game group. The treatment was rated significantly more enjoyable by both parents and children. Trends on pre-post Brain Maps indicate that the video game training may have advantages in creating more positive EEG effect in the therapeutic direction.
Epilepsy and Seizure Disorders- Substantial validation research has also been completed on neurofeedback for epilepsy or seizure disorder.
Several controlled studies have been completed, including three condition reversal studies. Several other open trials or case series have also been reported. A recent meta-analysis (combining results of numerous separate studies) indicated that 82% of patients demonstrated greater than 30% reduction in seizures, with an average greater than 50% reduction.
This outcome is all the more significant in that most of the participants included in these studies did not improve with standard medical care; for many, neurofeedback was the only alternative to surgery. Recent clinical experience has shown significantly improved outcomes using neurofeedback which is individually targeted at abnormalities in the degree of co-activation of different brain sites, as guided by coherence findings in the QEEG.
Anxiety and Neurofeedback- Research on neurofeedback for anxiety is less well developed than for ADHD and epilepsy.
Multiple small studies on generalized anxiety disorder, obsessive-compulsive disorder, phobic anxiety, and post-traumatic stress disorder have been published, with several controlled trials. Overall results show significant reduction in anxiety with neurofeedback, although several of the studies involved many fewer sessions than is used in clinical settings. Clinical trials with QEEG guided neurofeedback appear to show stronger benefit. With depression, several case studies have been published providing preliminary evidence of efficacy with major depression. A published open case series also suggests that QEEG guided neurofeedback training may have a larger effect size.
Substance Abuse- In research with adults with substance use disorder (PSUD) multiple random controlled trials (RCT’s) as well as uncontrolled studies have shown protocol specific changes in the EEG.
Improvements on measures of depression (self-rating), attention (using computerized tests of attention) and stress (physiological) have been noted. Several long-term follow-up studies showed a significant reduction in the one year abstinence/recidivism rate for those treated with neurofeedback compared to controls. Given that neurofeedback is medication free and has been shown to be effective with ADHD, a condition frequently also found with PSUD, neurofeedback appears to have particular value for these (PSUD with ADHD) patients where the risk of medication abuse is high.
Traumatic Brain Injury -Reviews of the literature on treatment for traumatic brain injury (TBI) and reading disabilities (RD) indicate that very few of the commonly used interventions have shown efficacy in formal research, and that the effect size of these techniques is usually quite small.
By contrast, preliminary research suggests that neurofeedback shows efficacy in the treatment of both of these conditions with a lare effect size. Several open case series and controlled studies (including one RCT) have shown significant benefits for neurofeedback with TBI primarily in adults, with improvements on measures of attention, executive function, cognitive flexibility, problem solving, information processing, verbal fluency, and depression, as well as in the EEG. Cessation and reduction of medication has also been reported as well as return to productive work. For RD, no formal studies have been published to date, although several studies of the effect of neurofeedback on ADHD have provided suggestive preliminary evidence of improved cognitive function.
Although more research needs to be completed, especially further follow-up studies assessing the maintenance of gains over time, clearly there is at the present time substantial research support for this intervention.
NASA Research: Counter-Response Training via Physiological Self-Regulation During Flight Simulation, (1997).
A new NASA training concept and technology is aimed at training pilots to maintain physiological equilibrium under emergency circumstances in an airplane cockpit.
NASA is working to adapt this concept to a full-scale cockpit simulator in future research and into a training approach for civilian and military professional pilots to reduce flight hazards resulting from impairment of pilot responses in aviation emergencies.
Excessive sympathetic autonomic nervous system (ANS) arousal can interfere with task performance of pilots during flight emergencies. Regular biofeedback training is hard to apply to this problem, because it is very situation-specific and biofeedback signals may distract the pilot from attention to flight tasks. NASA LaRC and Eastern Virginia Medical School developed a Stress Counter-Response Training method where biofeedback is integrated directly into ordinary flight tasks. Training aims at limiting deviations from optimal arousal levels through feedback during repeated stressful events in simulated flight.
With increased sophistication in technology, human performance has increasingly become a limiting factor in aviation safety. Both inattention and stress overload play a substantial role in impairing pilot performance and producing flight hazards. Biofeedback training can foreseeably help reduce the occurrence of these “hazardous states of awareness” by teaching pilots to maintain the necessary physiological conditions for good cognitive and psychomotor performance under the circumstances which are most likely to produce inattention or dysfunctional stress.
Pope, A. T., and Bogart, E. H. Identification of Hazardous Awareness States in Monitoring Environments. SAE Technical Paper No. 921136, SAE 1992 Transactions: Journal of Aerospace, Section 1 – Volume 101, 1993, pp. 449-457.
• Stressful events occur infrequently and between their occurrences the physiological functioning and performance of pilots may be normal.
• Regular biofeedback training is not appropriate.
• The problem is very situation-specific and hard to generate except in a flight simulator.
• In flight simulation of high-threat situations it is hard to add biofeedback task to the excessive cognitive demands on the pilot.
Progress in Efficacy Studies of EEG Biofeedback for ADHD. Roger deBeus, Ph.D., Eastern Virginia Medical School. Annual Meeting of the American Psychiatric Association Toronto, May 2006. deBeus et. al., is the first randomized controlled trial using a double-blind paradigm that incorporates a “sham” EEG Biofeedback treatment.
Fifty percent of the children were diagnosed with an inattentive type of ADHD; 50% with the combined type. Co-morbid conditions (oppositional-defiant disorder, conduct disorder, depression, social phobia, and obsessive-compulsive disorder) also were present in 46% of the children.
Participants were randomly assigned to either a bona fide EEG Biofeedback treatment (theta suppression, beta or SMR enhancement) or a “”sham” EEG Biofeedback condition in which rewards (e.g., movement on a Sony PlayStation game) were provided randomly. A total of 40 sessions were conducted.
Because a Sony PlayStation interface was used, neither the participants nor the therapist was aware of treatment condition (ie, bona fide versus sham EEG Biofeedback). Monitoring of EEG activity was conducted in both types of treatment. Twenty-eight of the children (equally represented in the two groups) were being treated with stimulant medications during their participation in treatment.
Statistical comparisons between the two groups revealed that the participants who received bona fide EEG Biofeedback were rated as demonstrating significantly less hyperactivity at home and school, improved attention at home, less anxiety, less depression and fewer complaints of minor physical problems at home, better adaptability to change, improved ability to work with others, and improved peer interactions, organizational skills, study habits, and a better attitude toward school (P < 0.01).
On computerized tests of attention, the children who had received bona fide EEG Biofeedback demonstrated significantly better scores than age-matched peers diagnosed with ADHD who received sham EEG Biofeedback (P < 0.01).
Demonstration of improvements in cortical arousal (reduced theta, increased beta or SMR) was evident only in the bona fide EEG Biofeedback groups (P < 0.01). Within this group, approximately one third of the patients were able to reduce dosage of medication.
ADD/ADHD, Learning and Development Disabilities, and Academic Cognitive Enhancement.
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text:
Ranganathan H, Venkateswara H, Chakraborty S, Panchanathan S, "Multi-label Deep Active Learning with Label Correlation", IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018.
Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S, "Deep Hashing Network for Unsupervised Domain Adaptation", IEEE conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017.
Raganathan H, Venkateswara H, Chakraborty S, Panchanathan S, "Deep Active Learning for Image Classification", IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017.
Tadayon R, McDaniel T, Panchanathan S, "A Survey of Multimodal Systems and Techniques for Motor Learning", Journal of Information Processing Systems, 2017.
Moore M, Venkateswara H, Panchanathan S, "A Reinforcement Learning Approach to Improving the Intelligibility of Dysarthric Speech", Neural Information System Processing (NIPS) Women in Machine Learning Workshop, Long Beach, CA, 2017.
Venkateswara H, Chakraborty S, McDaniel T, Panchanathan S, "Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptations", KnowPros Workshop Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, 2017.
Panchanathan S, Chakraborty S, McDaniel T, Tadayon R, Fakhri B, O'Connor N, Marsden M, Little S, McGuinness K, Monaghan D, "Enriching the Fan Experience in a Smart Stadium Using Internet of Things Technologies", International Journal of Semantic Computing, 2017.
Venkateswara H, Chakraborty S, Panchanathan S, "Deep-Learning Systems for Domain Adaptation in Computer Vision: Learning Transferable Feature Representations", IEEE Signal Processing MAgazine, 2017.
Tadayon A, Tadayon R, McDaniel T, Panchanathan S, "SmartGym: An Anticipatory System to Detect Body Compliance During Rehabilitative Exercise", Universal Access in Human-Computer Interaction: Human and Technological Environments, M. Antona, C. Stephanidis, Eds. Lecture Notes in Computer Science, 2017.
Raganathan H, Chakraborty S, S. Panchanathan, "Multimodal Emotion Recognition using Deep Learning Architectures", IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
S. Panchanathan, Chakraborty S, McDaniel T, "Social Interaction Assistant: A Person-Centered Approach to Enrich Social Interactions for Individuals with Visual Impairments", IEEE Journal of Selected Topics in Signal Processing, 2016.
Moore M, Panchanathan S, "TranslatAble: Giving Individuals with Complex Communication Needs a Voice through Speech and Gesture Recognition", The 18th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '16), Reno, NV, 2016.
Tadayon R, McDaniel T, Panchanathan S, "Autonomous Training Assistant: A System and Framework for Guided At-Home Motor Learning", The 18th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '16), Reno, NV, USA, 2016.
Monaghan D, Honohan F, Ahmadi A, McDaniel T, Tadayon R, Karpur A, Moran K, O'Connor NE, Panchanathan S, "A Multimodal Gamified Platform for Real-Time User Feedback in Sports Performance", ACM International Conference on Multimedia, Amsterdam, Netherlands, 2016.
Panchanathan S, Chakraborty S, McDaniel T, Tadayon R, "Person-Centered Multimedia Computing: A New Paradigm Inspired by Assistive and Rehabilitative Applications", IEEE Multimedia, 2016.
Ranganathan H, Chakraborty S, Panchanathan S, "Transfer of Multimodal Emotion Features in Deep Belief Networks", Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2016.
Zia J, Tadayon A, McDaniel T, Panchanathan S, "Utilizing Neural Networks to Predict Freezing of Gait in Parkinson's Patients", The 18th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '16), Reno, NV, USA, 2016.
Venkateswara H, Chakraborty S, Panchanathan S, "Nonlinear Embedding Transform for Unsupervised Domain Adaptation", Workshop on Transferring and Adapting Source Knowledge in Computer Vision (TASK-CV), European Conference on Computer Vision (ECCV), Amsterdam, Netherlands, 2016.
Venkateswara H, Lade P, Ye J, Panchanathan S, "Coupled Support Vector Machines for Supervised Domain Adaptation", ACM Multimedia Conference, Brisbane, Australia, 2015.
Tadayon A, Zia J, McDaniel T, Krishnamurthi N, Goldberg M, Anantuni L, Panchanathan S, "A Shoe Mounted System for Parkinsonian Gait Detection and Real-Time Feedback", 17th International Conference on Human-Computer Interaction (HCI International 2015), 2015.
Chakraborty S, Balasubramanian V, Sun Q, Panchanathan S, Ye J, "Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015.
Chakraborty S, Balasubramanian V, Panchanathan S, "Adaptive Batch Mode Active Learning", IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2015.
Chakraborty S, Balasubramanian V, Sankar RA, Panchanathan S, Ye J, "BatchRank: A Novel Batch Mode Active Learning Framework for Hierarchical Classification", ACM Conference on Knowledge Discovery and Data Mining (KDD), 2015.
Tadayon R, McDaniel T, Goldberg M, Robles-Franco PM, Zia J, Laff M, Geng M, Panchanathan S, "Interactive Motor Learning with the Autonomous Training Assistant: A Case Study", Human-Computer Interaction: Interaction Technologies, 2015.
Venkateswara H, Lade P, Lin B, Ye J, Panchanathan S, "Efficient Approximate Solutions to Mutual Information Based Global Feature Selection", IEEE International Conference on Data Mining (ICDM), 2015.
McDaniel T, Bala S, Rosenthal J, Tadayon R, Tadayon A, Panchanathan S, "Affective haptics for enhancing access to social interactions for individuals who are blind", HCI International, 2014.
Panchanthan R, McDaniel T, "Augmented motor learning and rehabilitation using vibrotactile feedback", (Best poster award) Medicine Meets Virtual Reality (MMVR21), Manhattan Beach, California, USA, 2014.
Tadayon R, Tadayon A, Karpur A, McDaniel T, Panchanathan S, "Geo-social mobile health gaming", Medicine Meets Virtual Reality (MMVR21), Manhattan Beach, California, USA, 2014.
Panchanathan S, McDaniel T, "Person-centered accessible technologies and computing solutions through interdisciplinary and integrated perspectives from disability research", Springer Journal on Universal Access in the Information Society Special Issue on User Experience and Access using Augmented and Multimedia Technologies, 2014.
P. Lade, T. McDaniel, S. Panchanathan, "Semantic Feature Projection for Continuous Emotion Analysis", ACM International Conference on Multimedia (ACMMM14), Orlando, Florida, 2014.
S. Yasmin, T. McDaniel, S. Panchanathan, "A Haptic-Based Application for Active Exploration of Facial Expressions by the Visually Impaired", 10th International Symposium on Visual Computing (ISVC), Las Vegas, Nevada, 2014.
R. Tadayon, S. Panchanathan, T. McDaniel, B. Fakhri, M. Laff, "A toolkit for motion authoring and motor skill learning in serious games", IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), 2014.
S. Bala, T. McDaniel, S. Panchanathan, "Visual-to-tactile mapping of facial movements for enriched social interactions", IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), 2014.
S. Yasmin, T. McDaniel, S. Panchanathan, "Haptic Mirror for Active Exploration of Facial Expressions by Individuals Who Are Blind", Proceedings of the ACM Symposium on Applied Perception, New York, NY, USA, 2014.
P. Lade, V. Balasubramanian, S. Panchanathan, H. Venkateswara, "Detection of changes in human affect dimensions using an adaptive temporal topic model", IEEE International Conference on Multimedia and Expo, San Jose, California, 2013.
H. Venkateswara, V. Balasubramanian, P. Lade, S. Panchanathan, "Multiresolution Match Kernels for Gesture Video Classification", IEEE International Conference on Multimedia and Expo (ICME), San, Jose, 2013.
P. Lade, V. Balasubramanian, S. Panchanathan, "Latent Facial Topics for Affect Analysis", ICME 2013 Workshop on Affective Analysis in Multimedia (AAM), San Jose, California, 2013.
T. McDaniel, S. Panchanathan, L.N. Viswanathan, "An evaluation of haptic descriptions for audio described films for individuals who are blind", IEEE International Conference on Multimedia and Expo, San Jose, California, 2013.
S. Panchanathan, T. McDaniel, V. Balasubramanian, "An Interdisciplinary Approach to the Design, Development and Deployment of Person-Centered Accessible Technologies", Third IEEE International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India, 2013.
Luster EL, T. McDaniel, B. Fakhri, J. Davis, M. Goldberg, S. Bala, S. Panchanathan, "Vibrotactile Cueing using Wearable Computers for Overcoming Learned Non-Use in Chronic Stroke", 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Venice, Italy, 2013.
E. Luster, "Approach for Capturing Movement Data and Monitoring Compliance during Stroke Rehabilitation", IEEE International Conference on Healthcare Informatics 2013 (ICHI 2013), Philadelphia, PA, 2013.
S. Chakraborty, J. Zhou, V. Balasubramanian, S. Panchanathan, I. Davidson, J. Ye, "Active Matrix Completion", 2013 IEEE International Conference on Data Mining (ICDM'13), Dallas, Texas, 2013.
P. Lade, V.N. Balasubramanian, S. Panchanathan, "Probabilistic Topic Models for Human Emotion Analysis", Neural Information Processing Society Workshop on Topic Models (NIPS), Lake Tahoe, Nevada, 2013.
R. Chattopadhyay, M. Jesunathadas, B. Poston, Santello M, "A Subject-Independent Method for Automatically Grading Electromyographic Features during a Fatiguing Contraction", Transactions on Biomedical Engineering (TBME), 2012.
R. Chattopadhyay, Z. Wang, W. Fan, I. Davidson, "Batch Mode Active Sampling based on Marginal Probability Distribution Matching", 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), 2012.
R. Chattopadhyay, Q. Sun, S. Panchanathan, W. Fan, "Multi-Source Domain Adaptation for Early detection of Fatigue using Surface Electromyogram Signals", Transactions on Knowledge Discovery from Data (TKDD), 2012.
T. McDaniel, M. Goldberg, S. Bala, B. Fakhri, S. Panchanathan, "Vibrotactile feedback of motor performance errors for enhancing motor learning", 20th ACM International Conference on Multimedia, Nara, Japan, 2012.
S. Panchanathan, T. McDaniel, V. Balasubramanian, "Person-centered accessible technologies: Improved usability and adaptation through inspirations from disability research", ACM Workshop on User Experience in e-Learning and Augmented Technologies in Education held in conjunction with the ACM International Conference on Multimedia, Nara, Japan, 2012.
S. Panchanathan, Krishna S, Balasubramanian VN, "Human-Centered Multimedia Computing: Inspirations through Enriching the Lives of Individuals with Sensory, Motor, Perceptual and Cognitive Disabilities", 7th International Conference on Trends in Industrial Measurements and Automation (TIMA 2011), Chennai, India, 2011.
R. Chattopadhyay, N.C. Krishnan, S. Panchanathan, "Hierarchical Domain Adaptation for SEMG Signal Classification across Multiple Subjects", IEEE Engineering in Medicine and Biology Society (EMBC’11), Boston, MA, USA, 2011.
R. Chattopadhyay, J. Ye, Panchanathan S, W. Fan, I. Davidson, "Multi-Source Domain Adaptation for Early detection of Fatigue using Surface Electro-myogram Signals", ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2011), San Diego, CA, USA, 2011.
R. Chattopadhyay, N.C. Krishnan, S. Panchanathan, "Topology Preserving Domain Adaptation for addressing Subject based Variability in SEMG signal", AAAI 2011 Spring Symposium on Computational Physiology, Stanford University, Menlo Park, CA, USA, 2011.
R. Chattopadhyay, Ye J, S. Panchanathan, "Transfer Learning Framework for Early Detection of Fatigue using Non-invasive Surface Electro-myogram signals (SEMG)", AAAI 2011 Spring Symposium on Computational Physiology, Stanford University, Menlo Park, CA, USA, 2011.
R. Chattopadhyay, G. Pradhan, S. Panchanathan, "Subject Independent Computational Framework for Myoelectric Signals", IEEE International Instrumentation and Measurement Technology Conference (IIMTC 2011), Binjiang, Hangzhou, China, 2011.
L.N. Viswanathan, T. McDaniel, S. Panchanathan, "Audio-Haptic Description in Movies", HCI International 2011, Orlando, Florida, 2011.
J. Rosenthal, N. Edwards, D. Villanueva, Krishna S, T. McDaniel, Panchanathan S, "Design, implementation, and case study of a pragmatic vibrotactile belt", IEEE Transactions on Instrumentation and Measurement, 2011.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "Dynamic Batch Mode Active Learning", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
M.J. Astrauskas, D. Hayden, Q. Yan, J. Black, "Student project aimed at helping the visually impaired take notes in class wins prize at Microsoft Imagine Cup", SIGACCESS Access. Comput., New York, NY, USA, 2011.
Q. Sun, R. Chattopadhyay, J. Ye, S. Panchanathan, "A Two-Stage Weighting Framework for Multi-Source Domain Adaptation", Twenty-Fifth Annual Conference on Neural Information Processing Systems 2011 (NIPS 11), 2011.
R. Chattopadhyay, S. Chakraborty, V.N. Balasubramanian, S. Panchanathan, "Optimization-based Domain Adaptation towards Person-Adaptive Classification Models", IEEE International conference on Machine Learning and Applications (ICMLA 2011), 2011.
S. Chakraborty, H. Venkateswara, V. Balasubramanian, S. Panchanathan, "Active Batch Selection for Fuzzy Classification in Facial Expression Recognition", IEEE International Conference on Machine Learning and Applications, ICMLA, 2011.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "Optimal Batch Selection for Active Learning in Multi-label Classification", ACM Multimedia Conference (ACM-MM), Phoenix, Arizona, 2011.
D. Hayden, M.J. Astrauskas, Q. Yan, L. Zhou, J. Black, "Note-taker 3.0: an assistive technology enabling students who are legally blind to take notes in class", The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility, New York, NY, USA, 2011.
T. McDaniel, M. Goldberg, D. Villanueva, L.N. Viswanathan, S. Panchanathan, "Motor learning using a kinematic-vibrotactile mapping targeting fundamental movements", 19th ACM international conference on Multimedia (MM '11), Scottsdale, Arizona, 2011.
S. Krishna, V.N. Balasubramanian, S. Panchanathan, "Enriching Social Situational Awareness in Remote Interactions: Insights and Inspirations from Disability Focused Research", ACM Multimedia 2010 (Brave New Ideas), Firenze, Italy, 2010.
S. Chakraborty, V.N. Balasubramanian, S. Panchanathan, "Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics", 9th International Conference on Machine Learning and Applications (ICMLA’10), Washington DC, USA, 2010.
V.N. Balasubramanian, S. Chakraborty, S. Krishna, S. Panchanathan, "Enhancing Social Interactions of Individuals with Visual Impairments: A Case Study for Assistive Machine Learning", Workshop on Machine Learning for Assistive Technologies at Neural Information Processing Systems (NIPS), Whistler, Canada, 2010.
V.N. Balasubramanian, J. Ye, S. Chakraborty, S. Panchanathan, "Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework", 9th International Conference on Machine Learning and Applications (ICMLA’10), Washington DC, USA, 2010.
V.N. Balasubramanian, S. Chakraborty, Panchanathan S, "Multiple Kernel Learning for Efficient Conformal Predictions", Workshop on New Directions in Multiple Kernel Learning at Neural Information Processing Systems (NIPS), Whistler, Canada, 2010.
P. Lade, N.C. Krishnan, S. Panchanathan, "Task Prediction in cooking activities using Hierarchical state space Markov Chain and Object based Task Grouping", CEA Workshop, International Symposium on Multimedia (ISM 2010), 2010.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "Learning from Summaries of Videos: Applying Batch Mode Active Learning to Face-based Biometrics", CVPR, San Franciso, CA, 2010.
S. Marcel, C. McCool, S. Chakraborty, V. Balasubramanian, "Mobile Biometry (MOBIO) Face and Speaker Verification Evaluation", ICPR, Istanbul,Turkey, 2010.
R. Chattopadhyay, J. Ye, S. Panchanathan, "A Generalized Machine Learning framework based on Transfer Learning techniques for early detection of Fatigue using Surface Electro-myogram signals (SEMG)", WIML workshop organized by Neural Information Processing Systems (NIPS 10), Vancouver, Canada, 2010.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "Batch Mode Active Learning for Biometric Recognition", SPIE Defense, Security & Sensing, Orlando, Florida, 2010.
R. Chattopadhyay, G. Pradhan, Panchanathan S, "Towards Fatigue and Intensity Measurement framework during continuous repetitive activities.", IEEE International Instrumentation and Measurement Technology Conference (IIMTC 2010), Austin, Texas, USA, 2010.
M. Goldberg, Krishna S, "Perception, Attention, Cognition, Social Interaction and Enhancement (PACSIE)", Center for Cognitive Ubiquitous Computing Seminar Series, Tempe, 2010.
M. Alzubaidi, A. Patel, S. Panchanathan, J. Black, "Reading a radiologist’s mind: monitoring rising and falling interest levels while scanning chest x-rays", SPIE Medical Imaging, San Diego, CA, 2010.
M. Alzubaidi, V. Balasubramanian, A. Patel, S. Panchanathan, J. Black, "What catches a radiologist’s eye? A comprehensive comparison of feature types for saliency prediction", SPIE Medical Imaging, San Diego, CA, 2010.
S. Krishna, T. McDaniel, S. Panchanathan, "Embodied Social Interaction Assistant", Tempe, AZ, 2010.
A. Venkatesan, N.C. Krishnan, S. Panchanathan, "Cost-sensitive Boosting for Concept Drift", ECML Workshop on Handling Concept Drift in Adaptive Information Systems (HaCDAIS), Barcelona, Spain, 2010.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics", IEEE International Conference on Machine Learning and Applications (ICMLA), 2010.
T. McDaniel, Krishna S, D. Villanueva, Panchanathan S, "A haptic belt for vibrotactile communication", IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), 2010.
L.N. Viswanathan, T. McDaniel, Krishna S, S. Panchanathan, "Haptics in audio described movies", IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), 2010.
D. Villanueva, T. McDaniel, S. Panchanathan, "InfoStone: bridging the gap between ambient and direct manipulation devices", IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), 2019.
T. McDaniel, D. Villanueva, Krishna S, Panchanathan S, "MOVeMENT: a framework for systematically mapping vibrotactile stimulations to fundamental body movements", IEEE International Symposium on Haptic Audio Visual Environments and Games, 2010.
S. Chakraborty, V. Balasubramanian, S. Panchanathan, "An Optimization Based Framework for Dynamic Batch Mode Active Learning", Workshop on Optimization for Machine Learning at Neural Information Processing Systems (NIPS), 2010.
G. Pradhan, R. Chattopadhyay, S. Panchanathan, "Processing Body Sensor Data Streams for Continuous Physiological Monitoring", ACM SIGKMM conference on Multimedia Information Retrieval (ACM MIR 2010), 2010.
T. McDaniel, D. Villanueva, Krishna S, Panchanathan S, "Vibrotactile stimulations for motor skill training", IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), 2010.
M. Vankipuram, K. Kahol, A. McLaren, S. Panchanathan, "A Virtual Reality Simulator for Orthopedic Basic Skills: A design and validation study", Journal of Biomedical Informatics, 2010.
D. Hayden, L. Zhou, M.J. Astrauskas, J. Black, "Note-taker 2.0: the next step toward enabling students who are legally blind to take notes in class", Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility, New York, NY, USA, 2010.
N.C. Krishnan, P. Lade, S. Panchanathan, "Activity gesture spotting using a threshold model based on Adaptive Boosting", IEEE International Conference on Multimedia Expo (ICME), Singapore, 2010.

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UNITED STUDENT AID FUNDS, INC., Petitioner, v. Francisco J. ESPINOSA.
Discharge of Student Loan Debt Absent Required Finding of Adversary Proceeding — Void Judgments.
In a Chapter 13 proceeding, a bankruptcy court’s order confirming discharge of a student loan debt absent undue hardship finding or adversary proceeding required by Bankruptcy Code and Rules of Bankruptcy Procedure is not a void judgment under Federal Rule of Civil Procedure 60(b)(4).
Madeleine C. Wanslee, Phoenix, AZ, for the petitioner by Toby J. Heytens for the U.S. as amicus curiae, by special leave of the Court, supporting the petitioner.
Michael J. Meehan, Tucson, AZ, for the respondent.
R. Ted Cruz, Allyson N. Ho, Morgan, Lewis & Bockius LLP, Houston, Texas, David B. Boodt, Joni M. Anderson, Julie E. Ragsdale, United Student Aid Funds, Inc., Indianapolis, IN, Charles W. Wirken, Counsel of Record, Madeleine C. Wanslee, Sean P. O’Brien, Gust Rosenfeld P.L.C., Phoenix, AZ, for Petitioner.
James L. Robinson, Jr., Robinson & Rylander, P.C., Tucson, AZ, for Petitioner.
Michael J. Meehan, Counsel of Record, Munger Chadwick P.L.C., Tucson, AZ, for Respondent.
John R. Kroger, Attorney General of Oregon, Mary H. Williams, Deputy Attorney General, Counsel of Record, Jerome Lidz, Solicitor General, Carolyn G. Wade, Assistant Attorney General, Salem, OR, for Amicus Curiae State of Oregon.
Under Chapter 13 of the Bankruptcy Code (Code), a debtor may obtain a discharge of certain government-sponsored student loan debts only if failure to discharge that debt would impose an “undue hardship” on the debtor and his dependents. 11 U.S.C. §§ 523(a)(8), 1328. The Federal Rules of Bankruptcy Procedure require bankruptcy courts to make this undue hardship determination in an adversary proceeding, see Rule 7001(6), which the party seeking the determination must initiate by serving a summons and complaint on his adversary, see Rules 7003, 7004, 7008. The debtor in this case filed a plan with the Bankruptcy Court that proposed to discharge a portion of his student loan debt, but he failed to initiate the adversary proceeding as required for such discharge. The creditor received notice of, but did not object to, the plan, and failed to file an appeal after the Bankruptcy Court subsequently confirmed the plan. Years later, the creditor filed a motion under Federal Rule of Civil Procedure 60(b)(4) asking the Bankruptcy Court to rule that its order confirming the plan was void because the order was issued in violation of the Code and Rules. We granted certiorari to resolve a disagreement among the Courts of Appeals as to whether an order that confirms the discharge of a student loan debt in the absence of an undue hardship finding or an adversary proceeding, or both, is a void judgment for Rule 60(b)(4) purposes.
Between 1988 and 1989, respondent Francisco Espinosa obtained four federally guaranteed student loans for a total principal amount of $13,250. In 1992, Espinosa filed a bankruptcy petition under Chapter 13. That Chapter permits individual debtors to develop a plan to repay all or a portion of their debts over a period of time specified in the plan. See Nobelman v. American Savings Bank, 508 U.S. 324, 327, 113 S.Ct. 2106, 124 L.Ed.2d 228 (1993); see also §§ 301(a), 1321; Fed. Rule Bkrtcy. Proc. 3015(b). A proposed bankruptcy plan becomes effective upon confirmation, see §§ 1324, 1325, and will result in a discharge of the debts listed in the plan if the debtor completes the payments the plan requires, see § 1328(a).
Espinosa’s plan listed his student loan debt as his only specific indebtedness. App. 15-18. The plan proposed to repay only the principal on that debt, stating that the remainder—the accrued interest— would be discharged once Espinosa repaid the principal. Id., at 26.As the Federal Rules of Bankruptcy Procedure require, the clerk of the Bankruptcy Court mailed notice and a copy of Espinosa’s plan to petitioner United Student Aid Funds, Inc. (United), the creditor to whom Espinosa owed the student loan debt.1 Id., at 34; see Rules 2002(b), (g)(2), 3015(d). In boldface type immediately below the caption, the plan stated: “WARNING IF YOU ARE A CREDITOR YOUR RIGHTS MAY BE IMPAIRED BY THIS PLAN.” Id., at 23. The plan also noted the deadlines for filing a proof of claim or an objection to the plan. Id., at 26-27.
In 2000, the United States Department of Education commenced efforts to collect the unpaid interest on Espinosa’s student loans.3 In response, Espinosa filed a motion in 2003 asking the Bankruptcy Court to enforce its 1997 discharge order by directing the Department and United to cease all efforts to collect the unpaid interest on his student loan debt.
to find undue hardship before discharging a student loan debt, §§ 523(a)(8), 1328(a), and with the Bankruptcy Rules, which require the court to make the undue hardship finding in an adversary proceeding, see Rule 7001(6). Second, United argued that its due process rights had been violated because Espinosa failed to serve it with the summons and complaint the Bankruptcy Rules require as a prerequisite to an adversarial proceeding. See Rules 7003, 7004, 7008.The Bankruptcy Court rejected both arguments, granted Espinosa’s motion in relevant part, denied United’s cross-motion, and ordered all claimants to cease and desist their collection efforts. United sought review in the District Court, which reversed. That court held that United was denied due process because the confirmation order was issued without service of the summons and complaint the Bankruptcy Rules require.
We granted certiorari. 557 U.S. ___, 129 S.Ct. 2791, 174 L.Ed.2d 289 (2009).
A void judgment is a legal nullity. See Black’s Law Dictionary 1822 (3d ed.1933); see also id., at 1709 (9th ed.2009). Although the term “void” describes a result, rather than the conditions that render a judgment unenforceable, it suffices to say that a void judgment is one so affected by a fundamental infirmity that the infirmity may be raised even after the judgment becomes final. See Restatement (Second) of Judgments 22 (1980); see generally id., § 12. The list of such infirmities is exceedingly short; otherwise, Rule 60(b)(4)’s exception to finality would swallow the rule.”A judgment is not void,” for example, “simply because it is or may have been erroneous.” Hoult v. Hoult, 57 F.3d 1, 6 (C.A.1 1995); 12 J. Moore et al., Moore’s Federal Practice § 60.44[a], pp. 60-150 to 60-151 (3d ed.2007) (hereinafter Moore’s). Similarly, a motion under Rule 60(b)(4) is not a substitute for a timely appeal. Kocher v. Dow Chemical Co., 132 F.3d 1225, 1229 (C.A.8 1997); see Moore’s § 60.44[a], at 60-150. Instead, Rule 60(b)(4) applies only in the rare instance where a judgment is premised either on a certain type of jurisdictional error or on a violation of due process that deprives a party of notice or the opportunity to be heard. See United States v. Boch Oldsmobile, Inc., 909 F.2d 657, 661 (C.A.1 1990); Moore’s § 60.44[a]; 11 Charles A. Wright, Arthur R. Miller, & Mary Kay Kane, Federal Practice & Procedure § 2862, p. 331 (2d ed.1995 and Supp.2009); cf. Chicot County Drainage Dist. v. Baxter State Bank, 308 U.S. 371, 376, 60 S.Ct. 317, 84 S.Ct. 329 (1940); Stoll v. Gottlieb, 305 U.S. 165, 171-172, 59 S.Ct. 134, 83 S.Ct. 104 (1938). The error United alleges falls in neither category.
Federal courts considering Rule 60(b)(4) motions that assert a judgment is void because of a jurisdictional defect generally have reserved relief only for the exceptional case in which the court that rendered judgment lacked even an “arguable basis” for jurisdiction. Nemaizer v. Baker, 793 F.2d 58, 65 (C.A.2 1986); see, e.g., Boch Oldsmobile, supra, at 661-662 (“[T]otal want of jurisdiction must be distinguished from an error in the exercise of jurisdiction, and … only rare instances of a clear usurpation of power will render a judgment void” (brackets and internal quotation marks omitted)).
loan debt is a precondition to obtaining a discharge order, not a limitation on the bankruptcy court’s jurisdiction. See, e.g., Arbaugh v. Y & H Corp., 546 U.S. 500, 515-516, 126 S.Ct. 1235, 163 L.Ed.2d 1097 (2006). Second, the requirement that a bankruptcy court make this finding in an adversary proceeding derives from the Bankruptcy Rules, see Rule Proc. 7001(6), which are “procedural rules adopted by the Court for the orderly transaction of its business” that are “not jurisdictional.” Kontrick v. Ryan, 540 U.S. 443, 454, 124 S.Ct. 906, 157 L.Ed.2d 867 (2004) (internal quotation marks omitted).
Espinosa’s failure to serve United with a summons and complaint deprived United of a right granted by a procedural rule. See Fed. Rule Bkrtcy. Proc. 7004(b)(3). United could have timely objected to this deprivation and appealed from an adverse ruling on its objection. But this deprivation did not amount to a violation of United’s constitutional right to due process. Due process requires notice “reasonably calculated, under all the circumstances, to apprise interested parties of the pendency of the action and afford them an opportunity to present their objections.” Mullane v. Central Hanover Bank & Trust Co., 339 U.S. 306, 314, 70 S.Ct. 652, 94 S.Ct. 865 (1950); see also Jones v. Flowers, 547 U.S. 220, 225, 126 S.Ct. 1708, 164 L.Ed.2d 415 (2006) (“[D]ue process does not require actual notice…”). Here, United received actual notice of the filing and contents of Espinosa’s plan. This more than satisfied United’s due process rights. Accordingly, on these facts, Espinosa’s failure to serve a summons and complaint does not entitle United to relief under Rule 60(b)(4).
Given the Code’s clear and self-executing requirement for an undue hardship determination, the Bankruptcy Court’s failure to find undue hardship before confirming Espinosa’s plan was a legal error. See Part III, infra. But the order remains enforceable and binding on United because United had notice of the error and failed to object or timely appeal.United’s response—that it had no obligation to object to Espinosa’s plan until Espinosa served it with the summons and complaint the Bankruptcy Rules require, Brief for Petitioner 33—is unavailing. Rule 60(b)(4) does not provide a license for litigants to sleep on their rights. United had actual notice of the filing of Espinosa’s plan, its contents, and the Bankruptcy Court’s subsequent confirmation of the plan. In addition, United filed a proof of claim regarding Espinosa’s student loan debt, thereby submitting itself to the Bankruptcy Court’s jurisdiction with respect to that claim. See Langenkamp v. Culp, 498 U.S. 42, 44, 111 S.Ct. 330, 112 L.Ed.2d 343 (1990) (per curiam). United therefore forfeited its arguments regarding the validity of service or the adequacy of the Bankruptcy Court’s procedures by failing to raise a timely objection in that court.
In issuing its judgment, however, the Court of Appeals looked beyond the narrow question whether the Bankruptcy Court’s order confirming Espinosa’s plan was void under Rule 60(b)(4). It canvassed other bankruptcy court decisions within the Circuit that presented a different question—whether a bankruptcy court presented with a debtor’s plan that proposes to discharge a student loan debt, in the absence of an adversary proceeding to determine undue hardship, should confirm the plan despite its failure to comply with the Code and Rules. The Court of Appeals noted that some Bankruptcy Courts had declined to confirm such plans “even when the creditor fail[ed] to object to the plan.” 553 F.3d, at 1205. The court disapproved that practice and overruled those cases, stating that bankruptcy courts mustconfirm a plan proposing the discharge of a student loan debt without a determination of undue hardship in an adversary proceeding unless the creditor timely raises a specific objection. Ibid. This, we think, was a step too far.
Hood, 541 U.S. at 450, 124 S.Ct. 1905.13 That is because § 1325(a) instructs a bankruptcy court to confirm a plan only if the court finds, inter alia, that the plan complies with the “applicable provisions” of the Code. § 1325(a) (providing that a bankruptcy court “shall confirm a plan” if the plan “complies with the provisions of” Chapter 13 and with “other applicable provisions of this title”); see Johnson v. Home State Bank, 501 U.S. 78, 87, 111 S.Ct. 2150, 115 L.Ed.2d 66 (1991); see also § 105(a) (authorizing bankruptcy courts to issue “any order, process, or judgment that is necessary or appropriate to carry out” the Code’s provisions).14 Thus, contrary to the Court of Appeals’ assertion, the Code makes plain that bankruptcy courts have the authority—indeed, the obligation—to direct a debtor to conform his plan to the requirements of §§ 1328(a)(2) and 523(a)(8).15We are mindful that conserving assets is an important concern in a bankruptcy proceeding. We thus assume that, in some cases, a debtor and creditor may agree that payment of a student loan debt will cause the debtor an undue hardship sufficient to justify discharge. In such a case, there is no reason that compliance with the undue hardship requirement should impose significant costs on the parties or materially delay confirmation of the plan. Neither the Code nor the Rules prevent the parties from stipulating to the underlying facts of undue hardship, and neither prevents the creditor from waiving service of a summons and complaint. See Fed. Rule Bkrtcy. Proc. 7004; Fed. Rule Civ. Proc. 4(k). But, to comply with § 523(a)(8)’s directive, the bankruptcy court must make an independent determination of undue hardship before a plan is confirmed, even if the creditor fails to object or appear in the adversary proceeding. See supra, at 1379.
United claims, the debtor can withdraw the plan and file another without penalty.We acknowledge the potential for bad-faith litigation tactics. But expanding the availability of relief under Rule 60(b)(4) is not an appropriate prophylaxis. As we stated in Taylor v. Freeland & Kronz, 503 U.S. 638, 112 S.Ct. 1644, 118 L.Ed.2d 280 (1992), “[d]ebtors and their attorneys face penalties under various provisions for engaging in improper conduct in bankruptcy proceedings,” id., at 644, 112 S.Ct. 1644; see Fed. Rule Bkrtcy. Proc. 9011. The specter of such penalties should deter bad-faith attempts to discharge student loan debt without the undue hardship finding Congress required. And to the extent existing sanctions prove inadequate to this task, Congress may enact additional provisions to address the difficulties United predicts will follow our decision.
1. United is a guaranty agency that administers the collection of federally guaranteed student loans in accordance with regulations promulgated by the United States Department of Education. See, e.g., 34 CFR § 682.200 et seq. (2009).
2. The discharge order contained an apparent clerical error that the courts below considered and addressed in adjudicating these proceedings. See n. 4, infra.
3. After Espinosa completed payments under the plan, United assigned Espinosa’s loans to the Department under a reinsurance agreement. After these proceedings began, United requested and received a recall of the loans from the Department. App. to Pet. for Cert. 63.
4. The one-page discharge order contained a paragraph that purported to exclude “`any debt … for a student loan'” from the discharge. 530 F.3d 895, 896 (9th Cir.2008). That provision appeared irreconcilable with the confirmation order, which contemplated the discharge of the interest on Espinosa’s student loan debt. Suggesting that the Bankruptcy Court may have automatically generated the discharge order without tailoring it to the terms of the confirmation order, the Court of Appeals remanded the case to the Bankruptcy Court to consider amending the discharge order to conform to the confirmation order. Id., at 899; see Fed. Rule Civ. Proc. 60(a) (authorizing a court to “correct a clerical mistake or a mistake arising from oversight or omission”). On remand, the Bankruptcy Court found that the text of its discharge order excepting Espinosa’s student loan debt from discharge “was inserted because of a clerical mistake” and struck that language from the order. App. 48.Although certain amici press the point, United has not challenged the substance of the Bankruptcy Court’s amendment to the order or asked us to consider whether such amendment was proper under Rule 60(a). See Brief for Petitioner 42; Reply Brief for Petitioner 20. Thus, we express no view on those issues. See Kamen v. Kemper Financial Services, Inc., 500 U.S. 90, 97, n. 4, 111 S.Ct. 1711, 114 L.Ed.2d 152 (1991) (noting that “we do not ordinarily address issues raised only by amici“).
5. In so doing, the Court of Appeals disagreed with two other Courts of Appeals. See In re Mersmann, 505 F.3d 1033, 1047-1049 (10th Cir.2007) (en banc); Whelton v. Educational Credit Management Corp., 432 F.3d 150, 154 (2d Cir.2005).
6. Three Courts of Appeals have reached the opposite conclusion on similar facts. See In re Ruehle, 412 F.3d 679, 682-684 (6th Cir. 2005); In re Hanson, 397 F.3d 482, 486 (7th Cir.2005); In re Banks, 299 F.3d 296, 302-303 (4th Cir.2002).
8. Because United brought this action on a motion for relief from judgment under Rule 60(b)(4), our holding is confined to that provision. We express no view on the terms upon which other provisions of the Bankruptcy Rules may entitle a debtor or creditor to postjudgment relief.
9. Subject to certain exceptions, Bankruptcy Rule 9024 makes Rule 60(b) applicable to Chapter 13 proceedings. One such exception provides that “a complaint to revoke an order confirming a plan may be filed only within the time allowed by” 11 U.S.C. § 1330. Fed. Rule Bkrtcy. Proc. 9024. Section 1330(a) imposes a 180-day time limit for a party to seek revocation of a confirmation order “procured by fraud.” Courts of Appeals disagree as to whether a Rule 60(b)(4) motion should be treated as a “complaint to revoke” a plan subject to § 1330’s time limit and substantive limitation to motions based on fraud. Compare Whelton, 432 F.3d, at 156, n. 2, with In re Fesq, 153 F.3d 113, 119 n. 8 (3rd Cir.1998). We need not settle that question, however, because the parties did not raise it in the courts below. And even under a theory that would treat United’s Rule 60(b)(4) motion as a “complaint to revoke” the plan, United’s failure to file its motion within § 1330(a)’s 180-day deadline and its failure to seek relief on the basis of fraud did not deprive those courts—and does not deprive us—of authority to consider the motion on the merits because those limitations are not jurisdictional. See Arbaugh v. Y & H Corp., 546 U.S. 500, 515-516, 126 S.Ct. 1235, 163 L.Ed.2d 1097 (2006); Reed Elsevier, Inc. v. Muchnick, ___ U.S. ___, ___-___, 130 S.Ct. 1237, ___ L.Ed.2d ___ (2010).
10. Sections 1328(a) and 523(a)(8) provide that student loan debt is dischargeable in a Chapter 13 proceeding if a court makes a finding of undue hardship. In contrast, other provisions in Chapter 13 provide that certain other debts are not dischargeable under anycircumstances. See, e.g., §§ 523(a)(1)(B), (C) (specified tax debts); § 523(a)(5) (domestic support obligations); § 523(a)(9) (debts “caused by” the debtor’s unlawful operation of a vehicle while intoxicated). We express no view on the conditions under which an order confirming the discharge of one of these types of debt could be set aside as void.
11. The Government suggests that § 523(a)(8)’s “self-executing” nature derives in part from the text of § 523(a), which states that “[a] discharge under section 727 … or 1328(b) of this title does not discharge an individual debtor from any debt,” including the student loan debts specified in paragraph (8) (emphasis added); see Brief for United States as Amicus Curiae 18; see also Reply Brief for Petitioner 1-2. That is not what we concluded in Hood and, in this case, would be irrelevant in any event.In Hood, we described as “`self-executing'” paragraph (8)’s instruction that student loan debt not be discharged “unless” an undue hardship determination is made. 541 U.S. at 450, 124 S.Ct. 1905. The “does not discharge” language in § 523(a), which applies generally to every enumerated paragraph in that section—and to which we never referred in Hood—was not relevant to our analysis. That is evident from the authority we cited to support our description of § 523(a)(8)’s condition as “`self-executing.'” E.g., id., at 450, 124 S.Ct. 1905 (citing S.Rep. No. 95-989, p. 79 (1978), which states that “[p]aragraph (8) … is intended to be self-executing” insofar as “the lender or institution is not required to file a complaint to determine the nondischargeability of any student loan” (emphasis added)).
In any event, the “does not discharge” language in § 523(a) is inapplicable to this case. Section 523(a) provides that “[a] discharge under section 727, 1141, 1228(a), 1228(b), or 1328(b) of [the Code] does not discharge an individual debtor from” the debts described in § 523(a)’s enumerated paragraphs. But Espinosa did not seek a discharge under “sections 727, 1141, 1228(a), 1228(b), or 1328(b).” He sought a discharge under § 1328(a), which provides that, upon completion of a Chapter 13 plan, a bankruptcy court “shall grant the debtor a discharge of all debts provided for by the plan …, except any debt … of the kind specified in … paragraph… (5), (8), or (9) of section 523(a).” (Emphasis added). Section 1328(a) thus incorporates by reference paragraph (8) of § 523(a), including that paragraph’s self-executing requirement for an undue hardship determination, but does not incorporate the “does not discharge” text of § 523(a) itself.
12. United relies on our decisions in United States ex rel. Wilson v. Walker, 109 U.S. 258, 3 S.Ct. 277, 27 S.Ct. 927 (1883), and Vallely v. Northern Fire & Marine Ins. Co., 254 U.S. 348, 41 S.Ct. 116, 65 S.Ct. 297 (1920), to argue otherwise. Those authorities are not controlling because they predate Rule 60(b)(4)’s enactment and because we interpreted the statutes at issue in those cases as stripping courts of jurisdiction—either over the parties, id., at 354-356, 41 S.Ct. 116, or the res, Wilson, supra, at 265-266, 3 S.Ct. 277—and United concedes that the statutory limit in this case is not jurisdictional. See supra, at 1377-1378.
13. This is essential to preserve the distinction between Congress’ treatment of student loan debts in § 523(a)(8) and debts listed elsewhere in § 523. Section 523(a)(8) renders student loan debt presumptively nondischargeable “unless” a determination of undue hardship is made. In contrast, the debts listed in § 523(c), which include certain debts obtained by fraud or “willful and malicious injury by the debtor,” § 523(a)(6), are presumptively dischargeable “unless” the creditor requests a hearing to determine the debt’s dischargeability. The Court of Appeals’ approach would subject student loan debt to the same rules as the debts specified in § 523(c), notwithstanding the evident differences in the statutory framework for discharging the two types of debt.
14. In other contexts, we have held that courts have the discretion, but not the obligation, to raise on their own initiative certain nonjurisdictional barriers to suit. See Day v. McDonough, 547 U.S. 198, 202, 209, 126 S.Ct. 1675, 164 L.Ed.2d 376 (2006) (statute of limitations); Granberry v. Greer, 481 U.S. 129, 134, 107 S.Ct. 1671, 95 L.Ed.2d 119 (1987) (habeas corpus petitioner’s exhaustion of state remedies). Section 1325(a) does more than codify this principle; it requires bankruptcy courts to address and correct a defect in a debtor’s proposed plan even if no creditor raises the issue.
15. Bankruptcy courts appear to be well aware of this statutory obligation. See, e.g., In re Mammel, 221 B.R. 238, 239 (Bkrtcy.N.D.Iowa 1998) (“[W]hether or not an objection is presently lodged in this case, the Court retains the authority to review this plan and deny confirmation if it fails to comply with the confirmation standards of the Code”).


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McCutcheon v. FEC — the next Citizens United?
Stephen Spaulding, of Common Cause and the Hill’s Congress blog, recently addressed the latest threat to the fight to get money out of politics.
On Tuesday, October 8, 2013, the Supreme Court is slated to hear arguments in the case of McCutcheon v. FEC . Shaun McCutcheon, the lead plaintiff, is challenging the $123,200 limit on contributions a single donor may make to federal candidates and political party committees during any two-year election cycle… If McCutcheon prevails, he and similarly wealthy donors soon will be able to write campaign checks of up to $3.6 million a pop.
McCutcheon, in a July interview, said that he believes individuals should have “more influence” (What?! Is this a democracy or an oligarchy?). He also stated that there needs to be a “real, real good reason” to limit individuals’ ability to give to campaigns.
In fact, there is a very good reason, and it was identified by the Supreme Court in prior rulings: to avoid corruption or the appearance of corruption. In Austin v. Michigan Chamber of Commerce (1990), the Court concluded that large contributions do represent corruptions or at least the appearance of corruption. Earlier, the decisions in Buckley v. Valeo (1976) and First National Bank of Boston v. Bellotti (1978) presented the argument that even the appearance of corruption can be devastating, as citizens begin to lose faith in the democratic process.
All three of these precedents, however, have already been challenged by the current Court’s decision in Citizens United v. FEC (2010). The February blog post, Citizens United Ruling Part 3, detailed the arguments of each side. Will the Citizens United precedent stand, or will the Court go back to previous arguments to find a good reason to limit campaign contributions? The next few months will tell.
This entry was posted in Citizens United, Supreme Court and tagged appearance of corruption, Austin v. Michigan Chamber of Commerce, Buckley v. Valeo, Citizens United, Citizens United v. Federal Election Commission, corruption, McCutcheon v. FEC, Supreme Court. Bookmark the permalink.

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Long Island University, Brooklyn campus.
York College, The City University of New York.
No institution that is eligible for and receives an award under section 1063b, 1102a, or 1136a of this title for a fiscal year shall be eligible to apply for a grant, or receive grant funds, under this section for the same fiscal year.
A prior section 1136b, Pub. L. 89–329, title XI, § 1103, as added Pub. L. 102–325, title XI, § 1101, July 23, 1992, 106 Stat. 785, related to applications for urban community service grants, prior to repeal by Pub. L. 105–244, § 3, title VII, § 702, Oct. 7, 1998, 112 Stat. 1585, 1803, effective Oct. 1, 1998.
Another prior section 1136b, Pub. L. 89–329, title XI, § 1103, as added Pub. L. 99–498, title XI, § 1101, Oct. 17, 1986, 100 Stat. 1569, related to requirements for economic development grant applications, prior to the general amendment of subchapter XI of this chapter by Pub. L. 102–325.
Another prior section 1136b, Pub. L. 89–329, title XI, § 1103, as added Pub. L. 96–374, title XI, § 1101, Oct. 3, 1980, 94 Stat. 1492, authorized grants to urban universities, prior to the general amendment of subchapter XI of this chapter by Pub. L. 99–498.
Another prior section 1136b, Pub. L. 89–329, title XI, § 1103, as added Pub. L. 90–575, title II, § 281, Oct. 16, 1968, 82 Stat. 1049; amended Pub. L. 92–318, title I, § 191(c), June 23, 1972, 86 Stat. 323; Pub. L. 94–482, title I, § 172, Oct. 12, 1976, 90 Stat. 2164; Pub. L. 96–49, § 11, Aug. 13, 1979, 93 Stat. 354, authorized appropriations for the law school clinical experiences program, prior to the general amendment of subchapter XI of this chapter by Pub. L. 96–374.
A prior section 724 of Pub. L. 89–329 was renumbered section 344, and is classified to section 1066c of this title.