High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Abstract

An emotional-valence detection method for a very–high wearable EEG-based system is proposed. 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. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition

“…Moreover, by providing real-time, long-term, and free-living dynamic information, new non-invasive wearable solutions for EEG monitoring would support the differential diagnosis of pathological conditions characterized by transitory changes in focal brain activity, including epileptic and non-epileptic disorders, such as the loss of consciousness (i.e., cardiovascular syncope) and parasomnias (i.e., REM behavior disorder) [ 6 , 7 ]. As a testament to the importance of portability, in the last few years, several portable EEG devices have appeared on the market [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], including in-ear EEG sensors [ 17 , 18 ]. However, none of these devices has successfully addressed the dual challenge of providing a battery lifespan longer than 12 h and ensuring sustained comfort for prolonged in-home applications.…”

Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications

“…Finally, it is worth mentioning the EEG-based emotional valence detection performed by Apicella et al with the AbMedica Helmate, a wireless, ultra-light EEG headband able to record at 512 Hz and transmit 8 EEG channels. Each trial lasted 30 s, but no information on the battery life is provided [ 17 ].…”