Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface

By Shangkai Gao, Xiaorong Gao. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005

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A review of channel selection algorithms for EEG signal processing (2015, methodology): “Wang et al. [41] proposed a channel reduction method in motor imagery, in which the prominent channels in this method were selected using the maximum of spatial pattern vectors obtained with the CSP algorithm.”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, methodology): “(CSP) algorithm is also used for channel selection [13], [14], whereby the channels are directly selected according to their CSP coefficients without deriving the features corresponding to each channel.”
An end-to-end deep learning approach to MI-EEG signal classification for BCIs (2018, methodology): “The dominating approach for the task of MI-EEG classification is the Common Spatial Patterns (CSP) algorithm (Wang et al., 2005; Grosse-Wentrup & Buss, 2008) and variations thereof, like Common Spatio-Spectral Patterns (CSSP) (Aghaei et al., 2016), Filter Bank CSP (FBCSP) (Ang et al., 2008;…”
LSTM-Based EEG Classification in Motor Imagery Tasks (2018, background): “It aims to find a spatial filter w which maximizes the variance of spatially filtered data in one class while minimizing the variance of filtered data in another class [4], [23].”