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标题：EEG-based detection of emotional valence towards a reproducible measurement of emotions
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作者：Andrea Apicella ; Pasquale Arpaia ; Giovanna Mastrati 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2021
卷号：11
DOI：10.1038/s41598-021-00812-7
语种：English
出版社：Springer Nature
摘要：A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%.