A real-time wearable emotion detection headband based on EEG measurement
A real-time wearable emotion detection headband based on EEG measurement
A real-time emotion detection system based on electroencephalogram (EEG) measurement has been realised by means of an emotion detection headband coupled with printed signal acquisition electrodes and open source signal processing software (OpenViBE). Positive and negative emotions are the states classified and the Theta, Alpha, Beta and Gamma frequency bands are selected for the signal processing. It is found that, by using a combination of Power Spectral Density (PSD), Signal Power (SP) and Common Spatial Pattern (CSP) as the features, the highest subject-dependent accuracy (86.83%) and independent accuracy (64.73%) is achieved, when using Linear Discrimination Analysis (LDA) as the classification algorithm. The standard deviation of the results is 5.03. The electrode locations were then improved for the detection of emotion, by moving them from F1, F2, T3 and T4 to A1, F2, F7 and F8. The subject-dependent accuracy, using the improved locations, increased to 91.75% from 86.83% and 75% of participants achieved a classification accuracy higher than 90%, compared with only 16% of participants before improving the electrode arrangement.
Emotion sensing, EEG, Wearable, Printing, Real time
614-621
Wei, Yang
c6d13914-4f35-459c-8c25-8f8b77b7c5b3
Wu, Yue
c5742488-4034-4697-acea-1df46dc0441f
Tudor, Michael
46eea408-2246-4aa0-8b44-86169ed601ff
15 August 2017
Wei, Yang
c6d13914-4f35-459c-8c25-8f8b77b7c5b3
Wu, Yue
c5742488-4034-4697-acea-1df46dc0441f
Tudor, Michael
46eea408-2246-4aa0-8b44-86169ed601ff
Wei, Yang, Wu, Yue and Tudor, Michael
(2017)
A real-time wearable emotion detection headband based on EEG measurement.
Sensors and Actuators A: Physical, 263, .
(doi:10.1016/j.sna.2017.07.012).
Abstract
A real-time emotion detection system based on electroencephalogram (EEG) measurement has been realised by means of an emotion detection headband coupled with printed signal acquisition electrodes and open source signal processing software (OpenViBE). Positive and negative emotions are the states classified and the Theta, Alpha, Beta and Gamma frequency bands are selected for the signal processing. It is found that, by using a combination of Power Spectral Density (PSD), Signal Power (SP) and Common Spatial Pattern (CSP) as the features, the highest subject-dependent accuracy (86.83%) and independent accuracy (64.73%) is achieved, when using Linear Discrimination Analysis (LDA) as the classification algorithm. The standard deviation of the results is 5.03. The electrode locations were then improved for the detection of emotion, by moving them from F1, F2, T3 and T4 to A1, F2, F7 and F8. The subject-dependent accuracy, using the improved locations, increased to 91.75% from 86.83% and 75% of participants achieved a classification accuracy higher than 90%, compared with only 16% of participants before improving the electrode arrangement.
Text
Wearable emotion detection headband based on EEG measurement
- Accepted Manuscript
More information
Accepted/In Press date: 10 July 2017
e-pub ahead of print date: 17 July 2017
Published date: 15 August 2017
Keywords:
Emotion sensing, EEG, Wearable, Printing, Real time
Identifiers
Local EPrints ID: 413118
URI: http://eprints.soton.ac.uk/id/eprint/413118
ISSN: 0924-4247
PURE UUID: 4aa6a335-9eb1-4fc1-b22b-4d6310ab014d
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Date deposited: 15 Aug 2017 16:30
Last modified: 16 Mar 2024 05:38
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Contributors
Author:
Yang Wei
Author:
Yue Wu
Author:
Michael Tudor
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