Abstract
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.
Highlights
Brain-computer interfaces (BCIs) provide new communication and control channels that do not depend on the brain’s normal output channels of peripheral nerves and muscles [1]
The BCI research aims at the development of a system that allows direct control of a computer application or a neuroprosthesis, solely by human intentions reflected by certain brain signals [2]
We mainly focus on noninvasive, electroencephalogram- (EEG-) based BCI systems which can be used as tools of communication for the disabled or for healthy subjects who might be interested in exploring a new path of human-machine interfacing
Summary
Brain-computer interfaces (BCIs) provide new communication and control channels that do not depend on the brain’s normal output channels of peripheral nerves and muscles [1]. We mainly focus on noninvasive, electroencephalogram- (EEG-) based BCI systems which can be used as tools of communication for the disabled or for healthy subjects who might be interested in exploring a new path of human-machine interfacing. A physically disabled person with controlling his thoughts has potential to use the mental processes for communication. The feasibility of this communication depends on the extent to which the EEGs associated with these mental processes can be reliably recognized automatically. The electrophysiological phenomena investigated most in the quest for an automatic discrimination of mental states are event-related potential (EP) [3], and localized changes in spectral power of spontaneous EEG related to sensorimotor processes [4, 5]. Based on event-related modulations of the pericentral μ- or β-rhythms of sensorimotor cortices
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