Identification of time-frequency EEG features modulated by force direction in arm isometric exertions

B. Nasseroleslami, H. Lakany, B. A. Conway

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

Electroencephalographic (EEG) activity associated with human motor tasks has been studied in time domain and time-frequency representations. Various classification and decoding techniques have been used to extract movement or motor task parameters from EEG such as direction of an isometrically exerted force. Identification of time and time-frequency regions that contain the highest directional information can considerably enhance the efficiency of decoding and classification algorithms. In this paper we have addressed this issue for directional arm isometric exertions to 4 different directions in horizontal plane. We have used the non-parametric Permutational ANOVA to identify time-frequency regions capturing the highest level of inter-group variance as a measure of directional information. There are information-rich regions in delta, theta, alpha, and beta bands after corresponding visual cues. Parietal regions show higher directional information during planning compared to execution. The results can be used for pattern classification and decoding of motor parameters in Brain-Computer-Interfacing (BCI) and BCI-rehabilitation.
Original languageEnglish
Title of host publication2011 5th International IEEE/EMBS conference on neural engineering (NER)
Place of PublicationNew York
PublisherIEEE
Pages422-425
Number of pages4
ISBN (Print)9781424441419
DOIs
Publication statusPublished - 2011

Publication series

NameInternational IEEE EMBS Conference on Neural Engineering
PublisherIEEE
ISSN (Print)1948-3546

Fingerprint

Decoding
Brain
Analysis of variance (ANOVA)
Patient rehabilitation
Pattern recognition
Planning

Keywords

  • movement
  • statistical significance
  • brain-computer interfaces
  • time-frequency
  • EEG features
  • modulated by force direction
  • arm isometric exertions

Cite this

Nasseroleslami, B., Lakany, H., & Conway, B. A. (2011). Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. In 2011 5th International IEEE/EMBS conference on neural engineering (NER) (pp. 422-425). (International IEEE EMBS Conference on Neural Engineering). New York: IEEE. https://doi.org/10.1109/NER.2011.5910576
Nasseroleslami, B. ; Lakany, H. ; Conway, B. A. / Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. 2011 5th International IEEE/EMBS conference on neural engineering (NER). New York : IEEE, 2011. pp. 422-425 (International IEEE EMBS Conference on Neural Engineering).
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Nasseroleslami, B, Lakany, H & Conway, BA 2011, Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. in 2011 5th International IEEE/EMBS conference on neural engineering (NER). International IEEE EMBS Conference on Neural Engineering, IEEE, New York, pp. 422-425. https://doi.org/10.1109/NER.2011.5910576

Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. / Nasseroleslami, B.; Lakany, H.; Conway, B. A.

2011 5th International IEEE/EMBS conference on neural engineering (NER). New York : IEEE, 2011. p. 422-425 (International IEEE EMBS Conference on Neural Engineering).

Research output: Chapter in Book/Report/Conference proceedingChapter

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N2 - Electroencephalographic (EEG) activity associated with human motor tasks has been studied in time domain and time-frequency representations. Various classification and decoding techniques have been used to extract movement or motor task parameters from EEG such as direction of an isometrically exerted force. Identification of time and time-frequency regions that contain the highest directional information can considerably enhance the efficiency of decoding and classification algorithms. In this paper we have addressed this issue for directional arm isometric exertions to 4 different directions in horizontal plane. We have used the non-parametric Permutational ANOVA to identify time-frequency regions capturing the highest level of inter-group variance as a measure of directional information. There are information-rich regions in delta, theta, alpha, and beta bands after corresponding visual cues. Parietal regions show higher directional information during planning compared to execution. The results can be used for pattern classification and decoding of motor parameters in Brain-Computer-Interfacing (BCI) and BCI-rehabilitation.

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Nasseroleslami B, Lakany H, Conway BA. Identification of time-frequency EEG features modulated by force direction in arm isometric exertions. In 2011 5th International IEEE/EMBS conference on neural engineering (NER). New York: IEEE. 2011. p. 422-425. (International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/NER.2011.5910576