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Kim Dremstrup

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Papers by Kim Dremstrup

Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials

An assistive robotic manipulator (ARM) can provide independence and improve the quality of life f... more An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steadystate visual evoked potentials (SSVEP) based BCI are among the best performing. Thus, this study investigates the design of a system for a full workspace control of a 7 degrees of freedom ARM. A SSVEP signal is elicited by observing a visual stimulus flickering at a specific frequency and phase. This study investigates the best combination of unique frequencies and phases to provide a 16-target BCI by testing three different systems offline. Furthermore, a fourth system is developed to investigate the impact of the stimulating monitor refresh rate. Experiments conducted on two subjects suggest that a 16-target BCI created by four unique frequencies and 16-unique phases provide the best performance. Subject 1 reaches a maximum estimated ITR of 235 bits/min while subject 2 reaches 140 bits/min. The findings suggest that the optimal SSVEP stimuli to generate 16 targets are a low number of frequencies and a high number of unique phases. Moreover, the findings do not suggest any need for considering the monitor refresh rate if stimuli are modulated using a sinusoidal signal sampled at the refresh rate.

The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity

Brain Research, Nov 1, 2017

A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the ... more A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the movement related cortical potential (MRCP) induces significant plasticity at the cortical level in healthy individuals and chronic stroke patients. Transferring this type of associative brain-computer interface (BCI) intervention into the clinical setting requires that the proprioceptive input is comparable to the techniques implemented during the rehabilitation process. These consist mainly of functional electrical stimulation (FES) and passive movement induced by an actuated orthosis. In this study, we compared these two interventions (BCI FES and BCI passive ) where the afferent input was timed to arrive at the motor cortex during the PN of the MRCP. Twelve healthy participants attended two experimental sessions. They were asked to perform 30 dorsiflexion movements timed to a cue while continuous electroencephalographic (EEG) data were collected from FP1, Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, and Pz, according to the standard international 10-20 system. MRCPs were extracted and the PN time calculated. Next, participants were asked to imagine the same movement 30 times while either FES (frequency: 20 Hz, intensity: 8-35 mAmp) or a passive ankle movement (amplitude and velocity matched to a normal gait cycle) was applied such that the first afferent inflow would coincide with the PN of the MRCP. The change in the output of the primary motor cortex (M1) was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior (TA) muscle and measuring the motor evoked potential (MEP). Spinal changes were assessed pre and post by eliciting the TA stretch reflex. Both BCI FES and BCI passive led to significant increases in the excitability of the cortical projections to TA (F (2,22) = 4.44, p = 0.024) without any concomitant changes at the spinal level. These effects were still present 30 minutes after the cessation of both interventions. There was no significant main effect of intervention, F (1,11) = 0.38, p = 0.550, indicating that the changes in MEP occurred independently of the type of afferent inflow. An afferent volley generated from a passive movement or an electrical stimulus arrives at the somatosensory cortex at similar times. It is thus likely that the similar effects observed here are strictly due to the tight coupling in time between the afferent inflow and the PN of the MRCP. This provides further support to the associative nature of the proposed BCI system.

Detection of movement-related cortical potentials based on subject-independent training

Medical & Biological Engineering & Computing, Jan 3, 2013

To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or complet... more 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.

Influence of attention alternation on movement-related cortical potentials in healthy individuals and stroke patients

Clinical Neurophysiology, 2017

Objective: In this study, we analyzed the influence of artificially imposed attention variations ... more Objective: In this study, we analyzed the influence of artificially imposed attention variations using the auditory oddball paradigm on the cortical activity associated to motor preparation/execution. Methods: EEG signals from Cz and its surrounding channels were recorded during three sets of ankle dorsiflexion movements. Each set was interspersed with either a complex or a simple auditory oddball task for healthy participants and a complex auditory oddball task for stroke patients. The amplitude of the movement-related cortical potentials (MRCPs) decreased with the complex oddball paradigm, while MRCP variability increased. Both oddball paradigms increased the detection latency significantly (p<0.05) and the complex paradigm decreased the true positive rate (TPR) (p = 0.04). In patients, the negativity of the MRCP decreased while pre-phase variability increased, and the detection latency and accuracy deteriorated with attention diversion. Attention diversion has a significant influence on MRCP features and detection parameters, although these changes were counteracted by the application of the laplacian method. Significance: Brain-computer interfaces for neuromodulation that use the MRCP as the control signal are robust to changes in attention. However, attention must be monitored since it plays a key role in plasticity induction. Here we demonstrate that this can be achieved using the single channel Cz.

Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

IEEE Transactions on Biomedical Engineering, Feb 1, 2014

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees

Sensors, Feb 21, 2022

Brain-computer interface performance may be reduced over time, but adapting the classifier could ... more Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation

Frontiers in Neuroscience, Jan 21, 2016

Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patie... more Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ∼70% for a detection latency of ∼200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.

Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 2019

An assistive robotic manipulator (ARM) can provide independence and improve the quality of life f... more An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steadystate visual evoked potentials (SSVEP) based BCI are among the best performing. Thus, this study investigates the design of a system for a full workspace control of a 7 degrees of freedom ARM. A SSVEP signal is elicited by observing a visual stimulus flickering at a specific frequency and phase. This study investigates the best combination of unique frequencies and phases to provide a 16-target BCI by testing three different systems offline. Furthermore, a fourth system is developed to investigate the impact of the stimulating monitor refresh rate. Experiments conducted on two subjects suggest that a 16-target BCI created by four unique frequencies and 16-unique phases provide the best performance. Subject 1 reaches a maximum estimated ITR of 235 bits/min while subject 2 reaches 140 bits/min. The findings suggest that the optimal SSVEP stimuli to generate 16 targets are a low number of frequencies and a high number of unique phases. Moreover, the findings do not suggest any need for considering the monitor refresh rate if stimuli are modulated using a sinusoidal signal sampled at the refresh rate.

B. Eeg Spectra Using Single and Bi-Frequency Stimulation..... 5

Plasticity following skilled learning and the implications for BCI performance

Brain Computer Interfaces (BCIs) rely on robust detection and classification algorithms for stabl... more Brain Computer Interfaces (BCIs) rely on robust detection and classification algorithms for stable and accurate device control. Here we investigate the effects of skilled motor learning on the stability of movement related cortical potentials (MRCP). Eight subjects were trained for 32 min on a task known to induce plasticity. Significant increases in performance were accompanied by significant increases in the variability of the EEG signal from two to one second prior to movement onset. The implications of these results for the use of the MRCP for online device control are discussed.

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Sensors, 2021

Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer inter... more Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on ...

Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: a feature and classifier investigation

Medical & Biological Engineering & Computing, 2020

Error-related potentials (ErrPs) have been proposed for designing adaptive brain-computer interfa... more Error-related potentials (ErrPs) have been proposed for designing adaptive brain-computer interfaces (BCIs). Therefore, ErrPs must be decoded. The aim of this study was to evaluate ErrP decoding using combinations of different feature types and classifiers in BCI paradigms involving motor execution (ME) and imagination (MI). Fifteen healthy subjects performed 510 (ME) and 390 (MI) trials of right/left wrist extensions and foot dorsiflexions. Sham BCI feedback was delivered with an accuracy of 80% (ME) and 70% (MI). Continuous EEG was recorded and divided into ErrP and NonErrP epochs. Temporal, spectral, discrete wavelet transform (DWT) marginals, and template matching features were extracted, and all combinations of feature types were classified using linear discriminant analysis, support vector machine, and random forest classifiers. ErrPs were elicited for both ME and MI paradigms, and the average classification accuracies were significantly higher than the chance level. The highest average classification accuracy was obtained using temporal features and a combination of temporal+DWT features classified with random forest; 89±9% and 83±9% for ME and MI, respectively. These results generally indicate that temporal features should be used when detecting ErrPs, but there is great intersubject variability, which means that user-specific features should be derived to maximize the performance.

Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch

Journal of Neural Engineering, 2019

Objective. Brain computer interfacing (BCI) is a promising method to control assistive systems fo... more Objective. Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI. Approach. Seven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented 4 (straight movements) or 8 commands (straight and diagonal movements). Main results. The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when 4-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for 4 and 8 commands. Significance. These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.

The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity

Brain Research, 2017

A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the ... more A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the movement related cortical potential (MRCP) induces significant plasticity at the cortical level in healthy individuals and chronic stroke patients. Transferring this type of associative brain-computer interface (BCI) intervention into the clinical setting requires that the proprioceptive input is comparable to the techniques implemented during the rehabilitation process. These consist mainly of functional electrical stimulation (FES) and passive movement induced by an actuated orthosis. In this study, we compared these two interventions (BCI FES and BCI passive ) where the afferent input was timed to arrive at the motor cortex during the PN of the MRCP. Twelve healthy participants attended two experimental sessions. They were asked to perform 30 dorsiflexion movements timed to a cue while continuous electroencephalographic (EEG) data were collected from FP1, Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, and Pz, according to the standard international 10-20 system. MRCPs were extracted and the PN time calculated. Next, participants were asked to imagine the same movement 30 times while either FES (frequency: 20 Hz, intensity: 8-35 mAmp) or a passive ankle movement (amplitude and velocity matched to a normal gait cycle) was applied such that the first afferent inflow would coincide with the PN of the MRCP. The change in the output of the primary motor cortex (M1) was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior (TA) muscle and measuring the motor evoked potential (MEP). Spinal changes were assessed pre and post by eliciting the TA stretch reflex. Both BCI FES and BCI passive led to significant increases in the excitability of the cortical projections to TA (F (2,22) = 4.44, p = 0.024) without any concomitant changes at the spinal level. These effects were still present 30 minutes after the cessation of both interventions. There was no significant main effect of intervention, F (1,11) = 0.38, p = 0.550, indicating that the changes in MEP occurred independently of the type of afferent inflow. An afferent volley generated from a passive movement or an electrical stimulus arrives at the somatosensory cortex at similar times. It is thus likely that the similar effects observed here are strictly due to the tight coupling in time between the afferent inflow and the PN of the MRCP. This provides further support to the associative nature of the proposed BCI system.

The Effect of Cutaneous and Deep Pain on the Electroencephalogram During Sleep—An Experimental Study

Sleep, 1997

The interaction between sleep and pain has been insufficiently studied, and no experiments have i... more The interaction between sleep and pain has been insufficiently studied, and no experiments have investigated whether pathologic sleep patterns as seen in pain patients can be replicated experimentally by welldefined pain stimuli. An experimental model would therefore be valuable for further studies on the interaction between pain and sleep. In this study, three well-defined experimental stimuli (muscle, joint, and cutaneous pain) were applied during sleep, and the electroencephalogram (EEG) pattern was quantified. The pain stimuli were applied during slow-wave sle'ep in 10 healthy subjects. Using nine surface recordings, the EEG was sampled before and during pain stimuli. Frequency analysis was performed, resulting in 10 EEG features describing the responses to pain. During the muscle-pain stimulus an arousal effect was observed and a decrease in delta (0.5-3.5 Hz) and sigma (12-14 Hz) as well as increases in alpha I (8-10 Hz) and beta (14.5-25 Hz) activities were seen. During joint pain, however, more universal EEG changes were seen with a decrease in the lowest frequency bands [delta, theta (3.5-8 Hz) and alpha I] and an increase in the higher frequencies [alpha 2 (10-12 Hz), sigma and beta bands]. No background EEG changes were observed during the cutaneous stimulus. There were several differences in the responses from the nine EEG channels, but no derivation seemed especially sensitive to detect the evoked changes. The study highlights the complexity of pain on the sleep EEG. The experimental model has shown that pain from different body structures, as well as signals from various EEG derivations, may give different responses in sleep microstructure.

Online detection and classification of movement kinetics

International Brain-Computer Interface Conference, BCI, 2014

Over the past years brain-computer interface (BCI) technology has been proposed as a means for ne... more Over the past years brain-computer interface (BCI) technology has been proposed as a means for neurorehabilitation. To induce Hebbian-associated-like plasticity, the movement-related cortical potential (MRCP) can be detected from the continuous brain activity to trigger timely appropriate inflow of somatosensory feedback from electrical stimulation. The aim of this study was to detect the MRCP online from the continuous brain activity and decode two types of movements that were performed with different levels of force and speed (2x50 movements). 5 healthy subjects and 1 stroke patient performed/attempted to perform the movements. The system correctly detected and classified 65±3 % and 51 % of the movements for the healthy subjects and patient, respectively. The findings suggest that it is possible to detect movements and decode kinetic information online. This may have implications for stroke rehabilitation where task variability may be introduced to optimize the retention of relearned movements.

Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation

Frontiers in Neuroscience, 2016

Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patie... more Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ∼70% for a detection latency of ∼200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.

Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface

Journal of neurophysiology, Jan 30, 2015

Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke pat... more Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here, we evaluate the effect and the underlying mechanisms of three BCI training sessions in a double-blind-sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesizes that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were allocated into two training groups. Movement-related cortical potentials (MRCPs) were detected using elecroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInon-associative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency and the excitab...

Comparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients

Journal of Neural Engineering, 2015

Objective. The possibility of detecting movement-related cortical potentials (MRCPs) at the singl... more Objective. The possibility of detecting movement-related cortical potentials (MRCPs) at the single trial level has been explored for closing the motor control loop with brain-computer interfaces (BCIs) for neurorehabilitation. A distinct feature of MRCPs is that the movement kinetic information is encoded in the brain potential prior to the onset of the movement which makes it possible to timely drive external devices to provide sensory feedback according to the efferent activity from the brain. The aim of this study was to compare methods for the detection (different spatial filters) and classification (features extracted from various domains) of MRCPs from continuous electroencephalography recordings from executed and imagined movements from healthy subjects (n=24) and attempted movements from stroke patients (n=6) to optimize the performance of MRCP-based BCIs for neurorehabilitation. Approach. The MRCPs from four cue-based tasks were detected with a template matching approach and a set of spatial filters, and classified with a linear support vector machine using the combination of temporal, spectral, time-scale, or entropy-based features. Main results. The best spatial filter (large Laplacian, LLSF) resulted in a true positive rate of 82±9%, 78±12% and 72±9% (with detections occurring ~200 ms before the onset of the movement) for executed, imagined and attempted movements (stroke patients). The best feature combination (temporal and spectral) led to pairwise classification of 73±9%, 64±10% and 80±12%. When the detection was combined with classification, 60±10%, 49±10% and 58±10% of the movements were both correctly detected and classified for executed, imagined and attempted movements. A similar performance for detection and classification was obtained with optimized spatial filtering. Significance. A simple setup with a LLSF is useful Comparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients for detecting cued movements while the combination of features from the time and frequency domain can optimize the decoding of kinetic information from MRCPs; this may be used in neuromodulatory BCIs.

Movement-related cortical potentials in paraplegic patients: abnormal patterns and considerations for BCI-rehabilitation

Frontiers in neuroengineering, 2014

Non-invasive EEG-based Brain-Computer Interfaces (BCI) can be promising for the motor neuro-rehab... more Non-invasive EEG-based Brain-Computer Interfaces (BCI) can be promising for the motor neuro-rehabilitation of paraplegic patients. However, this shall require detailed knowledge of the abnormalities in the EEG signatures of paraplegic patients. The association of abnormalities in different subgroups of patients and their relation to the sensorimotor integration are relevant for the design, implementation and use of BCI systems in patient populations. This study explores the patterns of abnormalities of movement related cortical potentials (MRCP) during motor imagery tasks of feet and right hand in patients with paraplegia (including the subgroups with/without central neuropathic pain (CNP) and complete/incomplete injury patients) and the level of distinctiveness of abnormalities in these groups using pattern classification. The most notable observed abnormalities were the amplified execution negativity and its slower rebound in the patient group. The potential underlying mechanisms ...