Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> The aim of the present study was to demonstrate the first time the non-invasive restoration of hand grasp function in a tetraplegic patient by electroencephalogram (EEG)-recording and functional electrical stimulation (FES) using surface electrodes. The patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movement. These beta bursts were analyzed and classified by a brain-computer interface (BCI) and the output signal used to control a FES device. The patient was able to grasp a cylinder with the paralyzed hand. <s> BIB001 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> Brain–machine interfaces are a promising approach for treating spinal cord injury-caused paralysis by rerouting control signals from the brain directly to the muscles. This paper demonstrates that monkeys can directly control stimulation of muscles using the activity of neurons in motor cortex, restoring goal-directed movement to a transiently paralysed arm. In addition, monkeys learned to use these artificial connections so that single neurons previously not associated with the movement could be used to control functional stimulation. <s> BIB002 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain-machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real-time. Linear decoders extracted 3D coordinates of leg joints and leg muscle EMGs from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again. <s> BIB003 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> Research on brain-machine interfaces has been ongoing for at least a decade. During this period, simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons have been used for direct, real-time control of various artificial devices. Brain-machine interfaces have also added greatly to our knowledge of the fundamental physiological principles governing the operation of large neural ensembles. Further understanding of these principles is likely to have a key role in the future development of neuroprosthetics for restoring mobility in severely paralysed patients. <s> BIB004 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> After complete spinal cord transections that removed all supraspinal inputs in adult rats, combinations of serotonergic agonists and epidural electrical stimulation were able to acutely transform spinal networks from nonfunctional to highly functional and adaptive states as early as 1 week after injury. Using kinematics, physiological and anatomical analyses, we found that these interventions could recruit specific populations of spinal circuits, refine their control via sensory input and functionally remodel these locomotor pathways when combined with training. The emergence of these new functional states enabled full weight-bearing treadmill locomotion in paralyzed rats that was almost indistinguishable from voluntary stepping. We propose that, in the absence of supraspinal input, spinal locomotion can emerge from a combination of central pattern-generating capability and the ability of these spinal circuits to use sensory afferent input to control stepping. These findings provide a strategy by which individuals with spinal cord injuries could regain substantial levels of motor control. <s> BIB005 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy. <s> BIB006 </s> Brain-machine interfaces: an overview <s> Functional electrical stimulation <s> Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~ 0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis. <s> BIB007
An alternative to using robotic arms and exoskeletons, is to connect cortical output to the subjects' own muscles, and use functional electrical stimulation (FES) as the method to produce movements. Several BMIs of this kind have been reported. Pfurtscheller and his colleagues attached a FES device to the forearm of a tetraplegic patient BIB001 . The BMI control was driven by EEG beta rhythms which the subject produced by imagining his foot move. The subjects learned to grasp objects using this BMI. Fetz and his colleagues demonstrated a similar BMI control using invasive recordings from monkey motor cortex BIB002 . After a The researchers at the Nicolelis laboratory were the first to test the possibility that kinematics of bipedal locomotion can be extracted from the primate cortex BIB003 . Figure 2 illustrates the settings of this experiment. Rhesus monkeys were trained to walk bipedally on a treadmill. While the monkeys performed this task, neuronal ensemble activity was recorded from cortical sensorimotor areas representing the lower limbs. Monkey leg movements were tracked with video cameras. A BMI decoder was set to extract leg kinematics from the cortical modulations. The decoding worked well for both forward and backward walking. Inspired by these results, Nicolelis and his colleagues founded the Walk Again Project, an international consortium for the development of the first cortically driven exoskeleton BIB004 . Nicolelis expects that such exoskeleton will restore mobility to patients suffering from various degrees of leg paralysis. A similar endeavor, called Mindwalker, started in Europe BIB006 . Additionally, Contreras Vidal and his colleagues proposed to drive a leg exoskeleton with EEGs. In support of this idea, they decoded gait kinematics from the EEGs recorded in human subjects walking on a treadmill BIB007 . In addition to cortically controlled BMIs for restoration of walking, there are alternative strategies based on the idea that locomotion can be induced by reactivation of the spinal CPG. Such reactivation was implemented in rats BIB005 . Locomotion was triggered in rats with spinal cord transections using a combination of epidural electrical stimulation with pharmacological effects of serotonergic agonists. Perhaps in the future, a hybrid, cortico-spinal BMI will be tested with voluntary aspects of locomotion controlled by cortical activity and automated patterns generated by the spinal CPG.