Source analysis in motor imagery EEG BCI applications

Aleksandr Zaitcev; Wei Liu; Greg Cook; Martyn Paley; Elizabeth Milne

doi:10.1049/PBHE016E_ch7

Source analysis in motor imagery EEG BCI applications

Aleksandr Zaitcev, Wei Liu, Greg Cook, Martyn Paley, Elizabeth Milne

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

Abstract

The presented analysis scenario shows how source reconstruction can be embedded in the EEG classification system and highlights its benefits for brain-computer interfaces (BCI) performance. Results of the analysis, trained classifiers, and selected feature indices can be directly used in BCI feedback training sessions. Linear inverse operators, such as WMNE, and sparse regions-of-interest are computationally simple enough to be applied in real-time settings.

Original language	English
Title of host publication	EEG Signal Processing
Publisher	Institution of Engineering and Technology
Pages	117-140
Number of pages	24
ISBN (Electronic)	9781785613708
DOIs	https://doi.org/10.1049/PBHE016E_ch7
Publication status	Published - 1 Jan 2019

Keywords

BCI feedback training
Bioelectric signals
Biology and medical computing
Brain-computer interfaces
Digital signal processing
EEG classification system
Electrical activity in neurophysiological processes
Electrodiagnostics and other electrical measurement techniques
Electroencephalography
Feature extraction
Feature indices
Linear inverse operators
Medical signal processing
Motor imagery EEG BCI applications
Real-time settings
Regions-of-interest
Signal processing and detection
Source analysis
Source reconstruction
Trained classifiers
User interfaces

ASJC Scopus subject areas

General Engineering
General Biochemistry,Genetics and Molecular Biology

More information

10.1049/PBHE016E_ch7

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abstract = "The presented analysis scenario shows how source reconstruction can be embedded in the EEG classification system and highlights its benefits for brain-computer interfaces (BCI) performance. Results of the analysis, trained classifiers, and selected feature indices can be directly used in BCI feedback training sessions. Linear inverse operators, such as WMNE, and sparse regions-of-interest are computationally simple enough to be applied in real-time settings.",

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AU - Cook, Greg

AU - Paley, Martyn

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Source analysis in motor imagery EEG BCI applications

Abstract

Keywords

ASJC Scopus subject areas

More information

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