A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces

Fabien Lotte (1, 2)

1 Potioc - Popular interaction with 3d content
2 LaBRI - Laboratoire Bordelais de Recherche en Informatique

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 analyse EEG signals as well as to stress the key points to understand when performing such an analysis.

Mots clés: brain-computer interfaces; EEG; signal processing; machine learning; classification; spatial filter; features

Domaines: Intelligence artificielle [cs.AI]; Biotechnologie; Biotechnologies; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Fabien Lotte. A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces. Eduardo Reck Miranda; Julien Castet. Guide to Brain-Computer Music Interfacing, Springer, 2014. ⟨hal-01055103⟩