Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface

A. R. Satti, D Coyle, G Prasad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages1731-1735
Number of pages6
DOIs
Publication statusPublished - Oct 2009
Event2009 IEEE International Conference on Systems, Man and Cybernetics - San Antonio, USA
Duration: 1 Oct 2009 → …

Conference

Conference2009 IEEE International Conference on Systems, Man and Cybernetics
Period1/10/09 → …

Fingerprint

Brain computer interface
Particle swarm optimization (PSO)
Brain
Electroencephalography
Frequency bands
Data acquisition

Cite this

@inproceedings{6278b8c01f0a42cfaabf1bfbfaca720c,
title = "Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface",
abstract = "Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.",
author = "Satti, {A. R.} and D Coyle and G Prasad",
year = "2009",
month = "10",
doi = "10.1109/ICSMC.2009.5346679",
language = "English",
pages = "1731--1735",
booktitle = "Unknown Host Publication",

}

Satti, AR, Coyle, D & Prasad, G 2009, Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface. in Unknown Host Publication. pp. 1731-1735, 2009 IEEE International Conference on Systems, Man and Cybernetics, 1/10/09. https://doi.org/10.1109/ICSMC.2009.5346679

Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface. / Satti, A. R.; Coyle, D; Prasad, G.

Unknown Host Publication. 2009. p. 1731-1735.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface

AU - Satti, A. R.

AU - Coyle, D

AU - Prasad, G

PY - 2009/10

Y1 - 2009/10

N2 - Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.

AB - Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.

U2 - 10.1109/ICSMC.2009.5346679

DO - 10.1109/ICSMC.2009.5346679

M3 - Conference contribution

SP - 1731

EP - 1735

BT - Unknown Host Publication

ER -