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Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Departament d’Informàtica, Universitat de València, Avda. de la Universidad, s/n, 46100-Burjasot, Spain
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Sensors 2019, 19(13), 2999; https://doi.org/10.3390/s19132999
Received: 4 June 2019 / Revised: 3 July 2019 / Accepted: 5 July 2019 / Published: 8 July 2019
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Abstract

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results. View Full-Text
Keywords: EEG; arousal detection; valence detection; data transformation; normalization EEG; arousal detection; valence detection; data transformation; normalization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Arevalillo-Herráez, M.; Cobos, M.; Roger, S.; García-Pineda, M. Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals. Sensors 2019, 19, 2999.

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