Abstract
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.
Highlights
IntroductionSome 16 million people per year experience stroke, from which about two-thirds survive worldwide [1]
According to the estimations, some 16 million people per year experience stroke, from which about two-thirds survive worldwide [1]
event-related desynchronization (ERD) was not detected in S4, S11, and S14, who were regarded as ERD blind [32]. e example of S4 is shown in Figure 3, in which no apparent decline was found in power spectral density during the experiment
Summary
Some 16 million people per year experience stroke, from which about two-thirds survive worldwide [1]. Stroke survivors suffer various deficits that generate disability in motor, perceptual, and cognitive functioning [2]. Among these disabilities, motor deficits have a large impact on managing everyday activities [3]. Recent studies have demonstrated that electroencephalogram- (EEG-) based braincomputer interface (BCI) has a great potential for motor rehabilitation in stroke patients [4,5,6], which is hypothesized that closing the loop between cortical activity (imagined or attempted motor intention) and actual movement can Journal of Healthcare Engineering restore functional corticospinal and corticomuscular connections [7]. In stroke rehabilitation with EEG-based BCI, SM1 is chosen to detect ERD [12,13,14,15]
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