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Classifying human emotional states using wireless EEG based ERP and functional connectivity measures

Classifying human emotional states using wireless EEG based ERP and functional connectivity measures
Classifying human emotional states using wireless EEG based ERP and functional connectivity measures
In this paper we present a systematic exploration to determine several EEG based features for classifying three emotional states (happy, fearful and neutral) pertaining to face perception. EEG data were acquired through a 19-channel wireless system from eight adults under two conditions – in a constrained position and involving head-body movements. The movement EEG data was pre-processed using an artifact reduction algorithm and both datasets were processed to extract neurophysiological features – ERP components and from functional connectivity measures. The functional connectivity measures were processed using a brain connectivity toolbox and gray level co-occurrence matrices to generate a total of 463 features. The feature set was split into: training dataset comprising of constrained and movement EEG data and test dataset comprising of only movement EEG data. A retrospective cross-validation approach was run on the training dataset in conjunction with two classifiers (LDA and SVM) and the ranked feature set, to select the best features using a sequential forward selection algorithm. The best features were further used to prospectively classify the three emotions in the test dataset. Our results show that we can successfully classify the emotions using LDA with an accuracy of 89% and using top 17 ranked features.
200-203
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Das, Saptarshi
0f460cec-6248-4f4c-84ac-8f215eaa048b
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Das, Saptarshi
0f460cec-6248-4f4c-84ac-8f215eaa048b
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Bono, Valentina, Biswas, Dwaipayan, Das, Saptarshi and Maharatna, Koushik (2016) Classifying human emotional states using wireless EEG based ERP and functional connectivity measures. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, United States. 24 - 27 Feb 2016. pp. 200-203 . (doi:10.1109/BHI.2016.7455869).

Record type: Conference or Workshop Item (Poster)

Abstract

In this paper we present a systematic exploration to determine several EEG based features for classifying three emotional states (happy, fearful and neutral) pertaining to face perception. EEG data were acquired through a 19-channel wireless system from eight adults under two conditions – in a constrained position and involving head-body movements. The movement EEG data was pre-processed using an artifact reduction algorithm and both datasets were processed to extract neurophysiological features – ERP components and from functional connectivity measures. The functional connectivity measures were processed using a brain connectivity toolbox and gray level co-occurrence matrices to generate a total of 463 features. The feature set was split into: training dataset comprising of constrained and movement EEG data and test dataset comprising of only movement EEG data. A retrospective cross-validation approach was run on the training dataset in conjunction with two classifiers (LDA and SVM) and the ranked feature set, to select the best features using a sequential forward selection algorithm. The best features were further used to prospectively classify the three emotions in the test dataset. Our results show that we can successfully classify the emotions using LDA with an accuracy of 89% and using top 17 ranked features.

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Published date: February 2016
Venue - Dates: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, United States, 2016-02-24 - 2016-02-27
Organisations: Electronic & Software Systems

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Local EPrints ID: 390190
URI: https://eprints.soton.ac.uk/id/eprint/390190
PURE UUID: 5534960d-0c24-4811-8ce2-2bcf61d0b4cc

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Date deposited: 19 Mar 2016 10:00
Last modified: 14 Oct 2019 18:42

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