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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 13; p. 2999
• Main Authors: Arevalillo-Herráez, Miguel, Cobos, Maximo, Roger, Sandra, García-Pineda, Miguel
• Format: Journal Article
• Language: English
• 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

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...