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TBE
2016

Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems

7 years 11 months ago
Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems
Abstract—Objective: Feature extraction is one of the most important steps in any brain–computer interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs (MI-BCI) has been the focus of several works in the past decade. This paper proposes a novel method, called separable common spatio-spectral patterns (SCSSP), for extraction of discriminant spatio-spectral EEG features in MI-BCIs. Methods: Assuming a binary classification problem, SCSSP uses a heteroscedastic matrix-variate Gaussian model for the multiband EEG rhythms, and seeks the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, SCSSP can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm. Results: The experimental results on two-class and multiclass motor-imagery data from publicly available BCI Competition datasets demonstrate that the proposed computationally ef...
Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Ko
Added 10 Apr 2016
Updated 10 Apr 2016
Type Journal
Year 2016
Where TBE
Authors Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Konstantinos N. Plataniotis
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