Abstract
The study investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortical potentials (MRCPs) and averaged sensorimotor rhythm associated with the two tasks were obtained from the EEG recordings. Moreover, offline single-trial EEG classification was performed with discrete wavelet transform for feature extraction and support vector machine for classification. The speed of the task was encoded in the time delay of peak negativity in the MRCPs, which was shorter for faster than for slower movements. The average single-trial misclassification rate between speeds was 30.4 ± 3.5% when the best scalp location and time interval were selected for each individual. The scalp location and time interval leading to the lowest misclassification rate varied among patients. The results indicate that the imagination of movements at different speeds is a viable strategy for controlling a brain-computer interface system by ALS patients.
Highlights
Many individuals suffer from motor impairment caused by traumatic brain injury, hypoxia, stroke, tumor, or neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS)
At approximately 0.6 s, the potential started to rebound for the fast movement, whereas the potential rebounded from approximately 1.3 s for the slow movement
This mental task may be an alternative strategy for controlling brain-computer interface (BCI) systems by these patients
Summary
Many individuals suffer from motor impairment caused by traumatic brain injury, hypoxia, stroke, tumor, or neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS). In these patients, sensory, emotional, and cognitive processing often remains largely intact despite extensive degeneration of the motor system. The control of slow cortical potentials requires long training time, these systems have allowed severely disabled and locked-in state patients to communicate messages of considerable length (Kübler et al, 2007; Neumann et al, 2003). We propose an alternative paradigm for BCI control and we investigate the feasibility of this control strategy
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