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Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors

Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors
Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors
In this paper we present a systematic exploration to formulate a predictive model of the human cognitive process with the changing environmental conditions at workplace. We select six different environmental conditions with small change in temperature/ventilation representative of realistic work environment having manual control. EEG data were acquired through 19-channel wireless system from three participants and CO2, Temperature, Relative humidity were recorded throughout the six conditions. The EEG data was pre-processed using an artifact reduction algorithm and 129 neurophysiological features were extracted from functional connectivity measures using complex network analysis. The environmental data were processed to generate 15 time/frequency domain features. Five best features selected through a ranking algorithm for all the variables across the six conditions were processed to formulate a model (environmental parameters as predictors) using retrospective 10-fold crossvalidation in conjunction with multiple linear regression. The model was prospectively evaluated over 10 runs on a test set to predict the EEG variable across the six conditions and parameters corresponding to the run producing least root mean square error were reported. Our exploration shows that the condition having no modulation of the ambient environmental parameters reflects the optimum condition for predicting the EEG features using the examined environmental parameters.
1-4
Biswas, Dwaipayan
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Bono, Valentina
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Scott-South, Michael
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Chatterjee, Shre
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Soska, Anna
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Snow, Steve
475bccef-a436-476f-ab42-a3581be78de8
Noakes, Catherine
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Barlow, Janet F.
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Maharatna, Koushik
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schraefel, m.c.
ac304659-1692-47f6-b892-15113b8c929f
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Scott-South, Michael
7c621f83-7e06-40b0-bf72-dabaef1ee4a1
Chatterjee, Shre
aaa84ab8-3968-42b1-a9e1-d2a2e03c7b0a
Soska, Anna
61bf571f-41d6-4352-9e72-8348255f5cd2
Snow, Steve
475bccef-a436-476f-ab42-a3581be78de8
Noakes, Catherine
cfa4b070-6ed5-4c2b-8cdb-5c342c77083b
Barlow, Janet F.
67e37dc0-37ca-4aee-b8ac-f0398ec745fe
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
schraefel, m.c.
ac304659-1692-47f6-b892-15113b8c929f

Biswas, Dwaipayan, Bono, Valentina, Scott-South, Michael, Chatterjee, Shre, Soska, Anna, Snow, Steve, Noakes, Catherine, Barlow, Janet F., Maharatna, Koushik and schraefel, m.c. (2016) Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors At The IEEE International Conference on Biomedical and Health Informatics (BHI2016), United States. 25 - 27 Feb 2016. , pp. 1-4.

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we present a systematic exploration to formulate a predictive model of the human cognitive process with the changing environmental conditions at workplace. We select six different environmental conditions with small change in temperature/ventilation representative of realistic work environment having manual control. EEG data were acquired through 19-channel wireless system from three participants and CO2, Temperature, Relative humidity were recorded throughout the six conditions. The EEG data was pre-processed using an artifact reduction algorithm and 129 neurophysiological features were extracted from functional connectivity measures using complex network analysis. The environmental data were processed to generate 15 time/frequency domain features. Five best features selected through a ranking algorithm for all the variables across the six conditions were processed to formulate a model (environmental parameters as predictors) using retrospective 10-fold crossvalidation in conjunction with multiple linear regression. The model was prospectively evaluated over 10 runs on a test set to predict the EEG variable across the six conditions and parameters corresponding to the run producing least root mean square error were reported. Our exploration shows that the condition having no modulation of the ambient environmental parameters reflects the optimum condition for predicting the EEG features using the examined environmental parameters.

Text Biswas_Paper.docx - Accepted Manuscript
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More information

Accepted/In Press date: 5 February 2016
Published date: 24 February 2016
Venue - Dates: The IEEE International Conference on Biomedical and Health Informatics (BHI2016), United States, 2016-02-25 - 2016-02-27
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 387013
URI: http://eprints.soton.ac.uk/id/eprint/387013
PURE UUID: 34f9b52a-0cf3-474b-8a94-5815af2dc2f2

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Date deposited: 08 Feb 2016 09:03
Last modified: 10 Nov 2017 22:27

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Contributors

Author: Dwaipayan Biswas
Author: Valentina Bono
Author: Michael Scott-South
Author: Shre Chatterjee
Author: Anna Soska
Author: Steve Snow
Author: Catherine Noakes
Author: Janet F. Barlow
Author: m.c. schraefel

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