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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...
Recent findings in neuroscience have shown that the spectral components of electroencephalogram (EEG) signals convey information regarding the mental task not only in their power but also in their phase. This calls for the utilization of complex-valued spectrum, instead of the commonly used power spectral density, in designing the brain computer interfaces. This paper studies the complex-valued spectrum...
Electroencephalogram (EEG) recordings of brain activities can be processed in order to augment the brain's cognitive, sensory, or motor functionality. A representative, yet analytically tractable, model is essential to EEG processing. Several studies have examined different statistical models for EEG power spectrum. But recent studies have shown that not only the power, but also the phase of the spectrum,...
Classification of mental tasks from electroencephalogram (EEG) signals has important applications in brain-computer interfacing (BCI). However, classification of the highly redundant and high-dimensional EEG signal, with high spatial and spectral correlations, is quite challenging. Therefore, the discriminant information, especially that of the first and second data moments, need to be extracted in...
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