A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Brain-computer interfaces are devices for enabling patients with severe motor disorders to communicate with the world. One method for operating such devices is to use movement-related potentials that are generated in the brain when the patient moves, or imagines a movement of, one of his limbs. An algorithm for detecting movement-related potentials using a small number of EEG channels was developed. This algorithm is a combination of the matched filter, a non-linear transformation previously developed as part of a similar detector, and a classifier. The algorithm was compared with previous designs of similar detectors by both theoretic analysis and off-line evaluation of performance on data recorded from five subjects. It is shown that the performance of the algorithm was superior to that of previous methods, improving the area under the receiver operating characteristic curve to 87.8%, an improvement of 25% compared with the best previously suggested detection method. Finally, the probable sources for false detections were identified, and possible ways to minimise them are proposed. <s> BIB001 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Abstract Objective To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Methods Twelve naive subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. Results The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Conclusions Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain–computer interface based on human natural movement, which might reduce the requirement of long-term training. Significance Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy. <s> BIB002 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Abstract We present a method for detecting movement intention from multichannel electroencephalographic (EEG) recordings. Movement intention is expressed as a slow negative deflection in amplitude of the EEG signal recorded above the motor cortex. This deflection is known as a movement-related cortical potential (MRCP). Detection of movement intention implies discrimination between MRCPs and noise. The signal-to-noise ratio of MRCPs was improved by an optimized spatial filter. Features were extracted with principal component analysis or locality preserving projections from the spatially filtered signals and classification between MRCPs and noise was performed with a k-nearest neighbors algorithm, modified by adjusting the decision rule to improve specificity, and a support vector machine approach. In one representative subject the sensitivity and specificity in detection were in the range 80–90% and 98–99.5%, respectively. The method seems promising for the development of asynchronous brain–computer interfaces (BCIs) based on MRCPs. <s> BIB003 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> To control the startup/shutdown of a conventional brain-computer interface (BCI) that is always running for daily use, we proposed and developed a new BCI system called a BCI master switch. We designed it with on/off switching functions by detecting the contingent negative variation (CNV) — related potentials. We chose CNV to improve the single-trial discrimination of user intentions to switch because CNV had a high signal-to-noise ratio and needed high concentration for its elicitation. We also applied a support vector machine (SVM) to improve the single-trial detection of CNV-related potentials. As the best parameters of SVM were estimated and applied, the offline evaluation's best performance achieved a CNV detection rate of 99.3% for the intention to switch and 2.1% for the intention not to switch. Remarkably, this performance was achieved from single-trial detection, imaginary response of user's intention without physical reaction, and the data from only one recording electrode. These results suggest that our proposed BCI system might work as a master switch by single-trial detection. <s> BIB004 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail. <s> BIB005 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of −66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback. <s> BIB006 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalographic recording that occurs about 2 s before voluntary movement production. The MRCP is thought to reflect the cortical processes involved in movement planning and movement preparation. In recent years, researchers have used this potential to investigate the processes involved in motor skill learning. Their findings indicate differences in the amplitude and onset times of the MRCP between experienced and novice performers, which have been attributed to long-term training in the experts. The authors discuss these findings critically and consider their implications for both future research and practice. <s> BIB007 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation. <s> BIB008 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> This paper proposes the development and experimental tests of a self-paced asynchronous brain-computer interfacing (BCI) system that detects movement related cortical potentials (MRCPs) produced during motor imagination of ankle dorsiflexion and triggers peripheral electrical stimulations timed with the occurrence of MRCPs to induce corticospinal plasticity. MRCPs were detected online from EEG signals in eight healthy subjects with a true positive rate (TPR) of 67.15±7.87% and false positive rate (FPR) of 22.05±9.07%. The excitability of the cortical projection to the target muscle (tibialis anterior) was assessed before and after the intervention through motor evoked potentials (MEP) using transcranial magnetic stimulation (TMS). The peak of the evoked potential significantly (P=0.02) increased after the BCI intervention by 53±43% (relative to preintervention measure), although the spinal excitability (tested by stretch reflexes) did not change. These results demonstrate for the first time that it is possible to alter the corticospinal projections to the tibialis anterior muscle by using an asynchronous BCI system based on online motor imagination that triggered peripheral stimulation. This type of repetitive proprioceptive feedback training based on self-generated brain signal decoding may be a requirement for purposeful skill acquisition in intact humans and in the rehabilitation of persons with brain damage. <s> BIB009 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> To allow a routinely use of brain–computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-related cortical potential (MRCP). EEG signals were recorded during ballistic ankle dorsiflexions executed (ME) or imagined (MI) by 20 healthy subjects, and attempted by five stroke subjects. These recordings were used to identify a template (as average over all subjects) for the initial negative phase of the MRCPs, after the application of an optimized spatial filtering used for pre-processing. Using this template, the detection accuracy (mean ± SD) calculated as true positive rate (estimated with leave-one-out procedure) for ME was 69 ± 21 and 58 ± 11 % on single trial basis for healthy and stroke subjects, respectively. This performance was similar to that obtained using an individual template for each subject, which led to accuracies of 71 ± 6 and 55 ± 12 % for healthy and stroke subjects, respectively. The detection accuracy for the MI data was 65 ± 22 % with the average template and 60 ± 13 % with the individual template. These results indicate the possibility of detecting movement intention without an individual training phase and without a significant loss in performance. <s> BIB010 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> One of the changes seen in electroencephalography (EEG) data preceding human voluntary movement is a cortical potential called readiness potential (RP). Detection of this potential can benefit researchers in clinical neurosciences for rehabilitation of malfunctioning brain and those working on brain-computer interfacing to develop a suitable mechanism to detect the intention of movement. Here, a constrained blind source extraction (CBSE) is attempted for detection of RP. A suitable constraint is defined and applied. The results are also compared with those of the traditional blind source separation in terms of true positive rate, false positive rate, and computation time. The results show that the CBSE approach in overall has superior performance. <s> BIB011 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Objective. In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. Approach. Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. Main results. The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. Significance. The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation. <s> BIB012 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation. <s> BIB013 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Objective. Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity 'number of misclassifications' and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied. Approach. Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a naive Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed. Main results. On average, 74.5 ± 13.8% and 82.2 ± 10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083). Significance. A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain–computer interface applications with this group of patients. <s> BIB014 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Techniques Utilized and Performance. <s> Abstract Objective Applications of brain–computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detection of gait initiation using movement related cortical potentials (MRCP). Methods The templates of MRCP were extracted from 9-channel scalp EEG during gait initiation in 9 healthy subjects. Independent component analysis (ICA) was used to remove artifacts, and the Laplacian spatial filter was applied to enhance the signal-to-noise ratio of MRCP. Following these pre-processing steps, a matched filter was used to perform single-trial detection of gait initiation. Results ICA preprocessing was shown to significantly improve the detection performance. With ICA preprocessing, across all subjects, the true positive rate (TPR) of the detection was 76.9 ± 8.97%, and the false positive rate was 2.93 ± 1.09 per minute. Conclusion The results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis. Significance The results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation. <s> BIB015
In recent years, slow cortical potentials captured the attention in the rehabilitation field. Several studies have been reported, which Computational and Mathematical Methods in Medicine 9 concentrated their attention intended for communication purposes. Yom-Tov and Inbar BIB001 developed an algorithm combining Matched Filter, a nonlinear transformation, and a classifier to detect MRCP using small number of EEG channels. The algorithm was compared with Mason-Birch lowfrequency asynchronous detector (LFASD) and optimum detector by both offline evaluation and theoretical analysis. The algorithm used by this study showed 25% improvement and the detector worked at a rate of 25 decisions s −1 as compared to 16 decisions s −1 in the LFASD. But the detector may not be useful in every application and failed to operate correctly, partly due to interference of MRPs from other limbs of the body and possibly due to imagined movements. These verdicts indicate that correctly identified features have a major role not only in discarding MRCPs due to movements that are not part of the BCI system, but also in determining the limb moved (or imagined movement) by the user. During sessions, training the subject to reduce body movement can probably attain better results. To detect movement planning,  employed a userspecific template matching structure as part of a method. In this study, the emphasis was more on movement detection than the prediction. Performing actual finger movement with cues, one electrode recorded BP waveform which was then used to build the template. However, inconsistency in performance between subjects was evident in this study which needs more investigation or perhaps a different methodology. From single trial EEG, BIB002 showed that effectual combinations of computational methods can deliver possible classification of human movement intention using large number of electrodes. The combinations of temporal filtering using power spectral density estimation and discrete wavelet transform, spatial filtering using ICA and surface Laplacian derivation, and classification methods using LMD, QMD, BC, and SVM provided higher performance than those of other combinations. Evaluation is recommended in order to check whether performance can be enhanced after training with feedback or not. The validity of OSF on imagination of isometric plantarflexion was confirmed in a study conducted by BIB003 . Features were extracted with PCA and classification was performed using NN and SVM. In this study, the TPRs were high (80-90%) but the method was verified on segmented data instead of ongoing EEG traces with one subject only. Kato et al. BIB004 designed a BCI master switch by detecting the CNV related potentials and performed both offline and online studies. In order to ameliorate the single-trial discrimination of user intentions to switch, CNV was employed due to its high SNR. Using only one electrode and performing four cued button press tasks, they also applied SVM to improve the single-trial detection of CNV-related potentials. Their online system did not discern between "intend to switch" and "do not intend to switch." This was maybe because of using default parameters of SVM in LIBSVM for the distinction of CNV-related potentials BIB004 BIB005 . The detection of movement intention from single trial MRCPs of movement imagination and movement execution was performed by BIB006 . The task performed by the subject was always the same (ankle dorsiflexion). They performed offline detection due to instrumentation limitation and provided the feasibility of the approach in stroke patients, along with the extensive analysis in healthy subjects. The accuracy of the detection of movement intention was measured by applying a similar spatial filtering technique. In this study, a portion of the negative phase (2 s) of the MRCP was used as a template. In order to improve SNR of MRCP, OSF was used (TPR of 82.5±7.81%). OSF outperformed large Laplacian spatial filter (TPR of 68.7 ± 14.9%) and CSP (TPR = 55.4 ± 14.01%). Lew et al. BIB008 explained that it is possible to predict the movement 500 ms before its occurrence. For the training phase, the signal prior to movement onset by 500 ms was used in comparison with 500 ms before the auditory cue. While a shifting window was implemented for the test phase and LDA was employed, their results showed maximum average TPR of 81% for left hand while 79% for right of stroke and control subjects. While for healthy subjects average TPR was 76 ± 7% with latency of −167±68 ms. This offline study employed large number of electrodes with small sample size of patients. Ahmadian et al. BIB011 showed the superiority of CBSE based algorithm in detection of brain potential compared with BSS based algorithm using LDA. Subjects performed cued button preprocessing. CBSE based algorithm took 0.26 s while BSS based algorithm took 51.90 s. All 128 channels EEG data was employed in the analysis. It was suggested that false detection rate can be reduced if BSS-based algorithm uses extracted sources which are mistaken with the shape of BP from other regions of brain. On the other hand, this amendment would increase the computation time. Motor intention could be detected from MRCP using the Matched Filter, with small latency and satisfactory accuracy. The same task was performed in BIB009 BIB013 BIB006 BIB010 . Niazi et al. performed analysis on both healthy and stroke patients BIB006 BIB010 . He investigated the possibility of eluding the individual training phase in the detection of movement intention. The detection accuracy with the average template for the motor imagery data was 65 ± 22% BIB010 and with the individual template was 60 ± 13% BIB010 . Jochumsen et al. BIB012 detected movement intentions and extracted distinct levels of speed and force of the intended movements. The temporal features were classified with an optimized SVM. This study evaluated performance when detection was combined with classification. The system correctly detected 81% of the movements. At the point of detection, system classified 75 ± 9% and 80 ± 10% when altering the force and speed, respectively. After combination of detector and classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of movements. Incorrectly detected and classified movements were about 21 ± 7% and 16 ± 9% while latency was 317 ± 73 ms before the movement onset. The authors included only healthy subjects and the signals were processed offline. The performance of system will decline if user is a patient due to severity of motor impairment, mood, and amount of training. Xu et al. BIB013 performed analysis on healthy subjects only. In this study, LPP-LDA showed higher accuracy and shorter latency than Matched Filter, having limitation that the classifier would not work when training trials were less than 15. The proposed algorithm had similar FPR for imagination and execution across all subjects. TPR for execution and imagery was greater than 80% and 70% in this study, making it significantly better than those with Matched Filter approach BIB009 BIB006 BIB010 . Also detection latency (315 ± 165 ms) was significantly shorter than that with Matched Filter (460 ± 123 ms) BIB009 BIB006 BIB010 . To further improve the results, BIB015 applied ICA and the LSF to improve the signal-to-noise ratio of MRCP. Following these preprocessing steps, Matched Filter was applied to perform single-trial detection of gait initiation. TPR was 76.9 ± 8.97%, and the false positive rate FPR was 2.93 ± 1.09 per minute. On a single trial basis, these results demonstrated the possibility of detecting the intention of gait initiation from EEG signals. In a recent study by BIB014 , six healthy persons and eight stroke patients performed upper limb self-paced reaching movements. This study used a classifier that combined the information acquired from analysis of the BP and ERD cortical processes. System validation was performed with the combined classifier (ERD and BP patterns) and equivalent classifiers (using either BP or ERD). The results obtained for healthy subjects were similar to BIB013 . However, the average latencies (healthy: −89.9 ± 349.2 ms and patients: 35.9 ± 352.3 ms) were less than BIB013 (315 ± 165 ms). These dissimilarities might be due to differences in the way subjects executed the task in each experiment such as changes between upper limb and lower limb cortical patterns, speed of movements among others, and length of the resting intervals between movements. The observed alterations could also be because of the combined use of the ERD and BP features anticipated here, which facilitates reduction in FPR and, as a result, enables the preference of more anticipative detection thresholds BIB014 . A complexity confronted in this paper involves the absence of similar studies in terms of purpose of detector, signal acquisition, limb movement, and number of electrodes. It should be noted that movements executed in different studies were not similar leading to variances in signal morphology and SNR. Ideally studies should be compared within the same context, that is, with similar protocol, users, and similar extraction of features. Although the mentioned studies have delivered a valuable insight into the prediction of MRCPs using different signal acquisition techniques, the framework of research is not without its impediments. One limitation relevant to most of the studies mentioned is the absence of clear ecological validity in the research, that is, "the extent to which an experimental situation mimics a real world situation" BIB007 . Presence of an ecological validity in a study means that the expertise used in the laboratory situation could be as equivalent as possible to the real skill the research is exploring and preferably identical.