EEG Signal Processing for Brain–Computer Interfaces

Chapter

Abstract

This chapter is focused on recent advances in electroencephalogram (EEG) signal processing for brain computer interface (BCI) design. A general overview of BCI technologies is first presented, and then the protocol for motor imagery noninvasive BCI for mobile robot control is discussed. Our ongoing research on noninvasive BCI design based not on recorded EEG but on the brain sources that originated the EEG signal is also introduced. We propose a solution to EEG-based brain source recovering by combining two techniques, a sequential Monte Carlo method for source localization and spatial filtering by beamforming for the respective source signal estimation. The EEG inverse problem is previously studded assuming that the source localization is known. In this work for the first time the problem of inverse modeling is solved simultaneously with the problem of the respective source space localization.

Abbreviations

2-D

two-dimensional

3-D

three-dimensional

AAR

adaptive auto regressive

ALS

amyotrophic lateral sclerosis

AR

auto regressive

BCI

brain-computer interface

BF

beamforming

BSS

blind source separation

ECoG

electrocorticographic

EEG

electroencephalography

EOG

electrooculography

ERD

event related desynchronization

ERS

event-related synchronization

HMM

hidden Markov model

ICA

independent component analysis

IEETA

Institute of Electrical Engineering and Telematics of Aveiro

IIR

infinite impulse response

IR

infrared

KF

Kalman filtering

LCMV

linearly constrained minimum variance

LDA

linear discriminant analysis

LH

lateral hypothalamus

LVQ

learning vector quantization

MC

memory complainer

MEG

magnetoencephalography

NN

neural network

PC

posterior cingulate cortex

PCA

principle component analysis

PET

positron emission tomography

PF

particle filter

PSD

power spectral density

ROI

region of interest

SMC

sequential Monte Carlo

SMR

sensorimotor rhythm

SNR

signal-to-noise ratio

SPECT

single photon emission computerized tomography

SVM

support vector machine

VEP

visual evoked potential

VEP

visually evoked potential

VIP

vasoactive intestinal peptide

fMRI

functional magnetic resonance imaging

fNIR

functional near-infrared system

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Department of Electronics Telecommunications and Informatics (DETI)University of AveiroAveiroPortugal
  2. 2.Department of Electronics, Telecommunications and InformaticsUniversity of Aveiro, Campus Universitário de SantiagoAveiroPortugal
  3. 3.Department of Computer ScienceUniversity of Arkansas at Little RockLittle RockUSA
  4. 4.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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