EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention

By Wei Tao, Chang Li, Rencheng Song, Juan Cheng, Yu Liu, Feng Wan, Xun Chen. IEEE Transactions on Affective Computing, 2023

485 citations recorded by the source corpus.

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 CNN is employed to extract the spatial information of encoded EEG signals. Then, to explore the temporal information of EEG signals, extended self-attention is integrated into an RNN to recode the importance based on intrinsic similarity in EEG signals. We conducted extensive experiments on the DEAP and DREAMER databases. The experimental results demonstrate that the proposed ACRNN outperforms state-of-the-art methods.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f19043d122e088780baca931d824719ded04de27

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

In Experimental and Cognitive Psychology

1. Bin Hu
2. Prayag Tiwari
3. Yazhou Zhang
4. Meihong Yang
5. Xiang Li
6. Zhigang Zhao
7. Pekka Marttinen
8. Neeraj Kumar

Citations in the corpus

• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[157] introduced attention mechanisms into the aforementioned CNN-LSTM hybrid DL models.”