Author(s): Fabien Lotte
This chapter presents an introductory overview and a tutorial of signal-processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in brain–computer interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial, and temporal information from noisy EEG signals (e.g., band-power features, spatial filters such as common spatial patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., linear discriminant analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals as well as to stress the key points to understand when performing such an analysis.