EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention
Hefei University of Technology · University of Macau · +1 more institution
Abstract
Emotion recognition based on electroencephalography (EEG) is a significant task in the brain-computer interface field. Recently, many deep learning-based emotion recognition methods are demonstrated to outperform traditional methods. However, it remains challenging to extract discriminative features for EEG emotion recognition, and most methods ignore useful information in channel and time. This article proposes an attention-based convolutional recurrent neural network (ACRNN) to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition. First, the proposed ACRNN adopts a channel-wise attention mechanism to adaptively assign the weights of different channels, and a…
Citation impact
- FWCI
- 23.98
- Percentile
- 100%
- References
- 65
Authors
7Topics & keywords
- Discriminative model
- Electroencephalography
- Computer science
- Artificial intelligence
- Convolutional neural network
- Pattern recognition (psychology)
- Brain–computer interface
- Speech recognition
- Reduced inequalities