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

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 approach...

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Published inSensors (Basel, Switzerland) Vol. 19; no. 13; p. 2999
Main Authors Arevalillo-Herráez, Miguel, Cobos, Maximo, Roger, Sandra, García-Pineda, Miguel
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 08.07.2019
MDPI
Subjects
Algorithms
Arousal - physiology
arousal detection
Artificial intelligence
Biometric identification
Biometrics
Data Analysis
data transformation
Databases, Factual
EEG
Electroencephalography
Electroencephalography - methods
Emotions
Emotions - physiology
Fourier transforms
Humans
Information sources
International conferences
Machine learning
Methods
Models, Biological
normalization
Physiology
Signal processing
Signal Processing, Computer-Assisted
Support Vector Machine
valence detection
Wavelet transforms
arousal detection
valence detection
data transformation
normalization
EEG
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19132999