Music emotion recognition with cEEGrid.

OBJECTIVE: Emotion decoding is a growing field of electroencephalography (EEG) research with numerous applications in areas such as healthcare, particularly when coupled with mobile EEG. However, research into mobile emotion decoding has been somewhat limited, especially regarding emotions elicited by music and how data splitting affects performance. The present study focuses on music emotion decoding with mobile (around-the-ear) EEG and the impact of the data splitting technique used. APPROACH: We collected the Decoding Auditory Attention and Musical Emotions with Ear-EEG dataset (DAAMEE), featuring both scalp EEG data (DAAMEE-s), and around-the-ear cEEGrid data (DAAMEE-c). DAAMEE featured several tasks, in one of which subjects listened to music and recorded their emotional state. We used the emotion task data, as well as the scalp EEG data from the existing DEAP dataset, for performance tests with various data splitting techniques and deep learning models. The tests focused on binary valence decoding, but the best-performing models were also applied for arousal, dominance, and three-dimensional valence-arousal-dominance (VAD) classification. MAIN RESULTS: Performance with DAAMEE-c was generally similar to performance with DAAMEE-s and DEAP, showing the viability of cEEGrid for emotion decoding. The results also demonstrate that using certain data splitting techniques may lead to performance inflation due to mechanisms such as temporal correlations between samples being exploited. SIGNIFICANCE: While these results motivate further research on mobile emotion decoding, future studies (for emotion decoding, and EEG classification generally), should take care to choose a data splitting paradigm that avoids overfitting.