Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients

Abstract

<?tex id="Q1" staff-cmt="&#x201C;In the last decade, technology-assisted stroke rehabilitation has been the focus of research. Electroencephalogram- (EEG-) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients since the closed loop between motor intention and the actual movement established by BCI can stimulate the neural pathways of motor control. Due to the deficits in the brain, motor intention expression may shift to other brain regions during and even after neural reorganization. The objective of this paper was to study the event-related desynchronization (ERD) topography during motor attempt tasks of the paretic hand in stroke patients and compare the classification performance using different channel-selection strategies in EEG-based BCI. Fifteen stroke patients were recruited in this study. A cue-based experimental paradigm was applied in the experiment, in which each patient was required to open the palm of the paretic or the unaffected hand. EEG was recorded and analyzed to measure the motor intention and indicate the activated brain regions. Support vector machine (SVM) combined with common spatial pattern (CSP) algorithm was used to calculate the offline classification accuracy between the motor attempt of the paretic hand and the resting state applying different channel-selection strategies. Results showed individualized ERD topography during the motor attempt of the paretic hand due to the deficits caused by stroke. Statistical analysis showed a significant increase in the classification accuracy by analyzing the channels showing ERD than analyzing the channels from the contralateral sensorimotor cortex (SM1). The results indicated that for stroke patients whose affected motor cortex is extensively damaged, the compensated brain regions should be considered for implementing EEG-based BCI for motor rehabilitation as the closed loop between the altered activated brain regions and the paretic hand can be stimulated more accurately using the individualized channel-selection strategy.

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training

“…The shape of EEG power data is (63, 1500, 53). To ensure the final input data contains all the information within the experiment, recorded EEG signals from all trials of the same state (Task and Rest) were averaged and amalgamated into one single matrix, avoiding biased prediction from experimental error [29].…”

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

“…Openvibe has been used for multiple applications and with different types of paradigms such as motor imagination to control a robotic arm or a robotic hand [30,31], motor imagination in neural plasticity with a wrist exoskeleton [32], with the P300 paradigm for the control of an electric chair [23], P300 for the control of a manipulator robot [35], surface electromyographic signals for the control of a functional electrical stimulator (FES) [82], neurorehabilitation [28,33,83], music [84], mobile robots [85] or processing with motor imagination or P300 [86][87][88][89] with different amplifiers [90].…”

Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients

“…The common problem in EEG-based BCI systems is the variance of classification performance among subjects depending on the ability to generate ERD in each individual. In addition, stroke patients usually show weak ERD in the ipsilesional sensorimotor cortex during cognitive tasks, which negatively affects the classification performance [19,36,37]. According to the abovementioned studies, AOMI is likely to be a better strategy than MI and AO for producing ERD.…”