Multiclass Support Vector Machines for EEG-Signals Classification

By Inan Güler, Elif Derya Übeyli. IEEE Transactions on Information Technology in Biomedicine, 2007

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A Review on the Computational Methods for Emotional State Estimation from the Human EEG (2013, background): “𝑏 denotes an offset parameter, 𝑥 𝑖 does training input vectors, and 𝛼 𝑖 does nonzero weights on each support vector [7, 77–80].”
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, methodology): “Principal component analysis (PCA), using 1% threshold, was employed to reduce the dimension of the feature set [44] resulting in three to four features in all cases.”
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, background): “Computer vision multi-classification techniques play a key role within objects [72], fingerprints [41] and sign language [8] recognition tasks, whereas in medicine, multiple categories are considered in problems such as cancer [6] or electroencephalogram signals [38] classification.”