EEG-based BCI system for decoding finger movements within the same hand. (17th April 2019)

• Record Type: Journal Article
• Authors: Alazrai, Rami; Alwanni, Hisham; Daoud, Mohammad I.
• Is Part Of: Neuroscience letters. Volume 698(2019)
• Page Start: 113
• Page End: 120
• Publication Date: 2019-04-17
• Subjects: Electroencephalography (EEG) -- Brain–computer interfaces (BCIs) -- Time-frequency distribution -- Finger movements -- Support vector machines; Neurology -- Periodicals; Neurology -- Periodicals; Research -- Periodicals; Neurologie -- Périodiques; Neuroanatomie -- Périodiques; Neuropharmacologie -- Périodiques; Neurophysiologie -- Périodiques; Neurology; Periodicals; Electronic journals; 617.48
• DOI: 10.1016/j.neulet.2018.12.045
• Languages: English
• ISSNs: 0304-3940

Abstract

Highlights: A new EEG-based BCI system is presented for decoding the movements of different fingers within the same hand. The EEG signals were analyzed using a Quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). A two-layer classification framework is developed to specify the moving finger along with movements performed by each finger within the same hand. The experimental results indicate the capability of the proposed system to decode finger movements within the same hand. Abstract: Decoding the movements of different fingers within the same hand can increase the control's dimensions of the electroencephalography (EEG)-based brain–computer interface (BCI) systems. This in turn enables the subjects who are using assistive devices to better perform various dexterous tasks. However, decoding the movements performed by different fingers within the same hand by analyzing the EEG signals is considered a challenging task. In this paper, we present a new EEG-based BCI system for decoding the movements of each finger within the same hand based on analyzing the EEG signals using a quadratic time-frequency distribution (QTFD), namely the Choi–William distribution (CWD). In particular, the CWD is employed to characterize the time-varying spectral components of the EEG signals and extract features that can capture movement-related information encapsulated within the EEG signals. The extracted CWD-based features are used to build a two-layer classification framework that decodes finger movements within the same hand. The performance of the proposed system is evaluated by recording the EEG signals for eighteen healthy subjects while performing twelve finger movements using their right hands. The results demonstrate the efficacy of the proposed system to decode finger movements within the same hand of each subject.