2022/10/25 by Mohammad Asif, Asif, Mohammad, Sudhakar Mishra +5 · 1 citation
Neuroscience · Psychology · #Arousal #Artificial Intelligence (cs.AI) #Cognitive psychology #Computer science #EEG and Brain-Computer Interfaces #Electroencephalography #Emotion and Mood Recognition #Emotion classification #Emotion recognition #FOS: Computer and information sciences #FOS: Electrical engineering #Feeling #Functional Brain Connectivity Studies #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Neuroscience #Psychology #Signal Processing (eess.SP) #Social psychology #Stimulus (psychology) #Valence (chemistry) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.02637
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simultaneously watching the emotional stimulus. We called these reported emotional feelings "Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS). We used EEG signals to classify emotional events on different combinations of Valence(V) and Arousal(A) dimensions and compared the results with benchmark datasets of DEAP and SEED. STFT is used for feature extraction and used in the classification model consisting of CNN-LSTM hybrid layers. We achieved significantly higher accuracy with our data compared to DEEP and SEED data. We conclude that having precise information about emotional feelings improves the classification accuracy compared to long-duration EEG signals which might be contaminated by mind-wandering.