E2ENNet: An end-to-end neural network for emotional brain-computer interface

Zhichao Han; Hongli Chang; Xiaoyan Zhou; Jihao Wang; Lili Wang; Yongbin Shao

doi:10.3389/fncom.2022.942979

E2ENNet: An end-to-end neural network for emotional brain-computer interface

Front Comput Neurosci. 2022 Aug 12:16:942979. doi: 10.3389/fncom.2022.942979. eCollection 2022.

Authors

Zhichao Han¹, Hongli Chang², Xiaoyan Zhou¹, Jihao Wang¹, Lili Wang¹, Yongbin Shao¹

Affiliations

¹ School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China.
² The Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Southeast University, Nanjing, China.

Abstract

Objectve: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.

Methods: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.

Results: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.

Conclusion: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.

Significance: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.

Keywords: depthwise separable convolution; electroencephalogram (EEG); emotional brain-computer interface; long short-term memory; neurocognitive.