Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems

Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems 170 177 IEEE Transactions on Biomedical Engineering (TBME)

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Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Konstantinos N. Plataniotis, University of Toronto

Brain-Computer Interface (BCI) systems aim to provide a non-muscular channel for the brain to control external devices using electrical activities of the brain. These BCIs can be used in various applications, such as controlling a wheelchair or neuroprosthesis for disabled individuals, navigation in virtual environment, and assisting healthy individuals in performing highly demanding tasks or controlling devices. Motor-imagery BCI systems in particular are based on decoding imagination of motor tasks, e.g., to control the movement of a wheelchair or a mouse curser on the computer screen and move it to the right or left directions by imagining right/left hand movement. In the past decade, there has been a growing interest in utilization of electroencephalogram (EEG) signals for non-invasive motor-imagery BCI systems, due to their low cost, ease of use, and widespread availability.

During motor-imagery tasks, multichannel EEG signals exhibit task-specific features in both spatial domain and spectral (or frequency) domain. This paper proposes a novel approach for extraction of discriminant spatio-spectral EEG features in Motor-imagery BCIs, which uses heteroscedastic matrix-variate Gaussian model for multiband EEG rhythms. In the proposed approach, EEG is first passed through a bank of bandpass filters to extract different bands of EEG rhythms. The resulting signal is then passed through a joint spatio-spectral feature extractor, called separable common spatio-spectral patterns (SCSSP), which directly operates on matrix-variate data.

SCSSP method jointly processes the data in both spectral and spatial domains, and hence can sort the extracted features across both domains. As a result, SCSSP does not require a separate subsequent dimensionality reduction stage, and its output can directly be passed to the classifier. Furthermore, SCSSP has relatively lower computational cost compared to similar algorithms such as filterbank-CSP. Proposed approach is particularly suitable for applications in which the computational power is limited, such as emerging wearable BCI systems.

Keywords: Brain computer interface, common spatial patterns, matrix-variate Gaussian, separability, spatio-spectral features

Amirhossein S. Aghaei

Amirhossein S. Aghaei (S’06 - M’14) is currently a Post-Doctoral Fellow with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada. His research interests are machine learning, pattern recognition, data analytics, statistical signal processing, and biomedical engineering. He received his Ph.D. and M.A.Sc degrees from the Department of Electrical and Computer Engineering at the University of Toronto, in 2008 and 2013 respectively, and received his B.Sc. degree in Electrical Engineering from Iran University of Science and Technology, Tehran, Iran, in 2006.

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