An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Kais Belwafi; Ridha Djemal; Fakhreddine Ghaffari; Olivier Romain

doi:10.1109/CCMB.2014.7020704

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Kais Belwafi, Ridha Djemal, Fakhreddine Ghaffari, Olivier Romain

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

We propose in this paper a novel approach of adaptive filtering of EEG signals. The filter adapts to the intrinsic characteristics of each person. The goal of the proposed method is to enhance the accuracy of the home devices system controlled by the thoughts related to two motor imagery actions. μ-rhythm and β-rhythm are the specific returned bands that contain the information. The main idea of the proposed method is to preserve the frequency bands of interest with a different value of the SNR on the stop-band. Our experimental results show the benefits of a suitable tuning of the filter on the accuracy of the classifier on the output of the EEG system. The proposed approach outperforms significantly performances reported in the literature and the effectively enhancement of the classification accuracy can reach up to 40% based only on filtering tuning.

Original language	British English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014
Subtitle of host publication	2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	121-126
Number of pages	6
ISBN (Electronic)	9781479945504
DOIs	https://doi.org/10.1109/CCMB.2014.7020704
State	Published - 23 Jan 2014
Event	2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Publication series

Name	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings

Conference

Conference	2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

Keywords

brain computer interface (BCI)
EEG filters optimization
ElectroEncephaloGram (EEG)
Motor imagery

Access to Document

10.1109/CCMB.2014.7020704

Cite this

Belwafi, K., Djemal, R., Ghaffari, F., & Romain, O. (2014). An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings (pp. 121-126). Article 7020704 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCMB.2014.7020704

Belwafi, Kais ; Djemal, Ridha ; Ghaffari, Fakhreddine et al. / An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 121-126 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings).

@inproceedings{c747af2fb888451e85ad1bf8f3dbbf82,

title = "An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery",

abstract = "We propose in this paper a novel approach of adaptive filtering of EEG signals. The filter adapts to the intrinsic characteristics of each person. The goal of the proposed method is to enhance the accuracy of the home devices system controlled by the thoughts related to two motor imagery actions. μ-rhythm and β-rhythm are the specific returned bands that contain the information. The main idea of the proposed method is to preserve the frequency bands of interest with a different value of the SNR on the stop-band. Our experimental results show the benefits of a suitable tuning of the filter on the accuracy of the classifier on the output of the EEG system. The proposed approach outperforms significantly performances reported in the literature and the effectively enhancement of the classification accuracy can reach up to 40% based only on filtering tuning.",

keywords = "brain computer interface (BCI), EEG filters optimization, ElectroEncephaloGram (EEG), Motor imagery",

author = "Kais Belwafi and Ridha Djemal and Fakhreddine Ghaffari and Olivier Romain",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014 ; Conference date: 09-12-2014 Through 12-12-2014",

year = "2014",

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doi = "10.1109/CCMB.2014.7020704",

language = "British English",

series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings",

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Belwafi, K, Djemal, R, Ghaffari, F & Romain, O 2014, An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings., 7020704, IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 121-126, 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/CCMB.2014.7020704

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. / Belwafi, Kais; Djemal, Ridha; Ghaffari, Fakhreddine et al.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 121-126 7020704 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Djemal, Ridha

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PY - 2014/1/23

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N2 - We propose in this paper a novel approach of adaptive filtering of EEG signals. The filter adapts to the intrinsic characteristics of each person. The goal of the proposed method is to enhance the accuracy of the home devices system controlled by the thoughts related to two motor imagery actions. μ-rhythm and β-rhythm are the specific returned bands that contain the information. The main idea of the proposed method is to preserve the frequency bands of interest with a different value of the SNR on the stop-band. Our experimental results show the benefits of a suitable tuning of the filter on the accuracy of the classifier on the output of the EEG system. The proposed approach outperforms significantly performances reported in the literature and the effectively enhancement of the classification accuracy can reach up to 40% based only on filtering tuning.

AB - We propose in this paper a novel approach of adaptive filtering of EEG signals. The filter adapts to the intrinsic characteristics of each person. The goal of the proposed method is to enhance the accuracy of the home devices system controlled by the thoughts related to two motor imagery actions. μ-rhythm and β-rhythm are the specific returned bands that contain the information. The main idea of the proposed method is to preserve the frequency bands of interest with a different value of the SNR on the stop-band. Our experimental results show the benefits of a suitable tuning of the filter on the accuracy of the classifier on the output of the EEG system. The proposed approach outperforms significantly performances reported in the literature and the effectively enhancement of the classification accuracy can reach up to 40% based only on filtering tuning.

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Belwafi K, Djemal R, Ghaffari F, Romain O. An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 121-126. 7020704. (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings). doi: 10.1109/CCMB.2014.7020704

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

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