EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm

• Published in: IEEE transactions on biomedical engineering Vol. 60; no. 8; pp. 2123 - 2132
• Main Authors: Robinson, Neethu, Vinod, A. P., Ang, Kai Keng, Tee, Keng Peng, Guan, Cuntai T.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Brain Mapping - methods; Brain-Computer Interfaces; Brain-computer interfaces (BCIs); Classification algorithms; common spatial patterns (CSPs); Correlation coefficient; discrete wavelet transform (DWT); Discrete wavelet transforms; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Evoked Potentials, Motor - physiology; Feature extraction; Filter banks; Filtering algorithms; Hand - physiology; Humans; Male; Motor Cortex - physiology; Movement - physiology; movement-related parameters; multiple linear regression; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Wavelet Analysis; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2013.2248153

A brain-computer interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This paper investigates the application of a noninvasive electroencephalography (EEG)based BCI to identify brain signal features in regard to actual hand movement speed. This provides a more refined control for a BCI system in terms of movement parameters. An experiment was performed to collect EEG data from subjects while they performed right-hand movement at two different speeds, namely fast and slow, in four different directions. The informative features from the data were obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spatial-spectral resolution. The applicability of these features to classify the two speeds and to reconstruct the speed profile was studied. The results for classifying speed across seven subjects yielded a mean accuracy of 83.71% using a Fisher Linear Discriminant (FLD) classifier. The speed components were reconstructed using multiple linear regression and significant correlation of 0.52 (Pearson's linear correlation coefficient) was obtained between recorded and reconstructed velocities on an average. The spatial patterns of the W-CSP features obtained showed activations in parietal and motor areas of the brain. The results achieved promises to provide a more refined control in BCI by including control of movement speed.