Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning

Sajeev Kunjan; T. W. Lewis; T. S. Grummett; D. M. W. Powers; K. J. Pope; S. P. Fitzgibbon; J. O. Willoughby

doi:10.1007/978-3-319-47103-7_29

Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning

Sajeev Kunjan, T. W. Lewis, T. S. Grummett, D. M. W. Powers, K. J. Pope, S. P. Fitzgibbon, J. O. Willoughby

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

1 Citation (Scopus)

Abstract

Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

Original language	English
Title of host publication	Brain Informatics and Health
Editors	Hesham Ali, Yong Shi, Giorgio A. Ascoli, Deepak Khazanchi, Michael Hawrylycz
Publisher	Springer International Publishing
Pages	295-303
Number of pages	9
ISBN (Print)	9783319471020
DOIs	https://doi.org/10.1007/978-3-319-47103-7_29
Publication status	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9919 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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Kunjan, S., Lewis, T. W., Grummett, T. S., Powers, D. M. W., Pope, K. J., Fitzgibbon, S. P., & Willoughby, J. O. (2016). Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. In H. Ali, Y. Shi, G. A. Ascoli, D. Khazanchi, & M. Hawrylycz (Eds.), Brain Informatics and Health (pp. 295-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9919 LNAI). Springer International Publishing. https://doi.org/10.1007/978-3-319-47103-7_29

Kunjan, Sajeev ; Lewis, T. W. ; Grummett, T. S. et al. / Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. Brain Informatics and Health. editor / Hesham Ali ; Yong Shi ; Giorgio A. Ascoli ; Deepak Khazanchi ; Michael Hawrylycz. Springer International Publishing, 2016. pp. 295-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning",

abstract = "Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.",

author = "Sajeev Kunjan and Lewis, {T. W.} and Grummett, {T. S.} and Powers, {D. M. W.} and Pope, {K. J.} and Fitzgibbon, {S. P.} and Willoughby, {J. O.}",

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doi = "10.1007/978-3-319-47103-7_29",

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Kunjan, S, Lewis, TW, Grummett, TS, Powers, DMW, Pope, KJ, Fitzgibbon, SP & Willoughby, JO 2016, Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. in H Ali, Y Shi, GA Ascoli, D Khazanchi & M Hawrylycz (eds), Brain Informatics and Health. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9919 LNAI, Springer International Publishing, pp. 295-303. https://doi.org/10.1007/978-3-319-47103-7_29

Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. / Kunjan, Sajeev; Lewis, T. W.; Grummett, T. S. et al.
Brain Informatics and Health. ed. / Hesham Ali; Yong Shi; Giorgio A. Ascoli; Deepak Khazanchi; Michael Hawrylycz. Springer International Publishing, 2016. p. 295-303 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9919 LNAI).

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

TY - CHAP

T1 - Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning

AU - Kunjan, Sajeev

AU - Lewis, T. W.

AU - Grummett, T. S.

AU - Powers, D. M. W.

AU - Pope, K. J.

AU - Fitzgibbon, S. P.

AU - Willoughby, J. O.

PY - 2016

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N2 - Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

AB - Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

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DO - 10.1007/978-3-319-47103-7_29

M3 - Chapter

SN - 9783319471020

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 295

EP - 303

BT - Brain Informatics and Health

A2 - Ali, Hesham

A2 - Shi, Yong

A2 - Ascoli, Giorgio A.

A2 - Khazanchi, Deepak

A2 - Hawrylycz, Michael

PB - Springer International Publishing

ER -

Kunjan S, Lewis TW, Grummett TS, Powers DMW, Pope KJ, Fitzgibbon SP et al. Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning. In Ali H, Shi Y, Ascoli GA, Khazanchi D, Hawrylycz M, editors, Brain Informatics and Health. Springer International Publishing. 2016. p. 295-303. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-47103-7_29

Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning

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