Abstract
In recent years, asynchronous brain computer interface (BCI) systems have been utilized in many domains such as robot controlling, assistive technology, and rehabilitation. In such BCI systems, movement intention detection algorithms are used to detect movement desires. In recent years, movement-related cortical potential (MRCP), an electroencephalogram (EEG) pattern representing voluntary movement intention, attracts wide attention in movement intention detection. Unfortunately, low MRCP detection accuracy makes the asynchronous BCI system impractical for real usage. In order to develop an effective MRCP detection algorithm, EEG data have to be properly preprocessed. In this work, we investigate the relationship and effects of three factors including frequency bands, spatial filters, and classifiers on MRCP classification performance to determine best settings. In particular, we performed a systematic performance investigation on combinations of five frequency bands, five spatial filters, and six classifiers. The EEG data were acquired from subjects performing series of self-paced ankle dorsiflexions. Analysis of variance (ANOVA) statistical test was performed on F1 scores to investigate effects of these three factors. The results show that frequency bands and spatial filters depend on each other. The combinations directly affect the F1 scores, so they have to be chosen carefully. The results can be used as guidelines for BCI researchers to effectively design a preprocessing method for an advanced asynchronous BCI system, which can assist the stroke rehabilitation.
Highlights
A brain computer interface (BCI) system is a system that translates human minds to control signals for external devices. ese external devices include simple feedback systems and more complex devices such as prosthetic organs [1]
Patients suffering from a brain disease including locked-in syndrome (LIS) or completely locked-in state (CLIS), as they cannot move, talk, or even blink, can get benefits from the BCI system [2]. erefore, large amount of research effort has been devoted to solving communication problem for these patients [1, 3,4,5]
There has been evidence based on the Hebbian theory [6] showing that motor function loss can be restored by brain plasticity induction through the BCI system for rehabilitation [7]. is phenomenon can be a great advantage for stroke patients, whose brain regions were partially destroyed by blood clots in the brain vessel
Summary
A brain computer interface (BCI) system is a system that translates human minds to control signals for external devices. ese external devices include simple feedback systems and more complex devices such as prosthetic organs [1]. There have been evidences showing that the recent BCI systems for stroke rehabilitation can induce brain plasticity in stroke patients [8,9,10], its limitation in terms of movement intention detection accuracy makes it not widely used. We make the first attempt to study effects and relationship among the 3 factors—frequency bands, spatial filtering techniques, and classifiers, on EEG data acquired in a self-paced manner for movement intention detection. Our results provide valuable insights towards a development in an effective movement intention detection algorithm for asynchronous BCI rehabilitation systems. (iii) is work explores a possibility of utilizing shapes of time series signals to detect MRCP from self-paced EEG data using unconventional time series mining techniques
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