CNN-BiLSTM and DC-IGN fusion model and piecewise exponential attenuation optimization: an innovative approach to improve EEG emotion recognition performance

TL;DR

This paper introduces a new model combining CNN-BiLSTM and DC-IGN with an optimized training strategy to improve emotion recognition from EEG signals.

Contribution

The novel fusion of CNN-BiLSTM and DC-IGN with piecewise exponential decay optimization for EEG emotion recognition is proposed.

Findings

The model achieved 94.35% accuracy on the SEED dataset.

It outperformed other models with 89.84% on DEAP-valence and 90.31% on DEAP-arousal.

The model's superiority was confirmed through subject-independent and learning rate experiments.

Abstract

EEG emotion recognition has important applications in human-computer interaction and mental health assessment, but existing models have limitations in capturing the complex spatial and temporal features of EEG signals. To overcome this problem, we propose an innovative model that combines CNN-BiLSTM and DC-IGN and fused both outputs for sentiment classification via a fully connected layer. In addition, we use a piecewise exponential decay strategy to optimize the training process. We conducted a comprehensive comparative experiment on the SEED and DEAP datasets, it includes traditional models, existing advanced models, and different combination models (such as CNN + LSTM, CNN + LSTM+DC-IGN). The results show that our model achieves 94.35% accuracy on SEED dataset, 89.84% on DEAP-valence, 90.31% on DEAP-arousal, which is significantly better than other models. In addition, we further verified the superiority of the model through subject independent experiment and learning rate scheduling strategy comparison experiment. These results not only improve the performance of EEG emotion recognition, but also provide new ideas and methods for research in related fields, and prove the significant advantages of our model in capturing complex features and improving classification accuracy.

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