The authors review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG).

The authors briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, they compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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