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Localizing event-related cortical sources is a key factor while developing a computationally efficient Brain Computer Interface (BCI). This paper proposes a unified application of wavelet-based Maximum Entropy on the Mean (wMEM), as a channel selection method, for classifying two motor imagery (MI) tasks using optimal electroencephalography (EEG) sources. The EEG data, which are collected from publicly...
Multi-channel electroencephalography (EEG) recordings require excessive computation and sometimes engender outliers, which make brain computer interface (BCI) systems inefficient. Thus, optimal channel selection becomes a key factor for developing a more comfortable BCI. This study emphasized on a time-frequency (T-F) coherence method, called as Wavelet Coherence (WC), for selecting lesser number...
Unification of spatial brain dynamics in multiclass brain computer interface (BCI) paradigm reduces computational latencies by using lesser number of electrodes from the sensorimotor regions of the brain. We employ reduced number of channels without compromising performance notably. We apply three spatial filtering methods, i.e., Common Spatial Pattern (CSP), Regularized Common Spatial Pattern (RCSP)...
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