Information Technology Reference
In-Depth Information
7
A Tutorial on EEG Signal-processing
Techniques for Mental-state
Recognition in Brain
-
Computer
Interfaces
Fabien Lotte
Abstract
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 classi
cation algorithms (e.g., linear discriminant analysis) used to
classify this information into a class of mental state. It also brie
y 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.
7.1
Introduction
One of the critical steps in the design of brain
computer interface (BCI) applications
based on electroencephalography (EEG) is to process and analyze such EEG signals
in real time, in order to identify the mental state of the user. Musical EEG-based BCI
applications are no exception. For instance, in (Miranda et al.
2011
), the application
had to recognize the visual target the user was attending to from his/her EEG signals,
in order to execute the corresponding musical command. Unfortunately, identifying
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