Paper Info
Title
Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism
Abstract
Deep learning (DL) technologies have recently shown great potential in emotion recognition based on electroencephalography (EEG). However, existing DL-based EEG emotion recognition methods are built on single-task learning, i.e., learning arousal, valence, and dominance individually, which may ignore the complementary information of different tasks. In addition, single-task learning involves a new round of training every time a new task appears, which is time consuming. To this end, we propose a novel method for EEG-based emotion recognition based on multi-task learning with capsule network (CapsNet) and attention mechanism. First, multitask learning can learn multiple tasks simultaneously while exploiting commonalities and differences across tasks, it can also obtain more data from different tasks, which can improve generalization and robustness. Second, the innovative structure of the CapsNet enables it to effectively characterize the intrinsic relationship among various EEG channels. Finally, the attention mechanism can change the weight of different channels to extract important information. In the DEAP dataset, the average accuracy reached 97.25%, 97.41%, and 98.35% on arousal, valence, and dominance, respectively. In the DREAMER dataset, average accuracy reached 94.96%, 95.54%, and 95.52% on arousal, valence, and dominance, respectively. Experimental results demonstrate the efficiency of the proposed method for EEG emotion recognition.
Year
DOI
Venue
2022
10.1016/j.compbiomed.2022.105303
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Deep learning, Emotion recognition, Electroencephalogram, Multi-task learning, Capsule network
Journal
143
ISSN
Citations 
PageRank 
0010-4825
1
0.35
References 
Authors
0
7
Name
Order
Citations
PageRank
chang li128219.50
Bin Wang21788246.68
Silin Zhang310.35
Yu Liu449230.80
Rencheng Song5156.03
Juan Cheng66211.53
Xun Chen745852.73