A novel deep learning architecture combining an autoencoder, 2D CNN, and LSTM achieved emotion classification accuracies of 90.04% for arousal, 89.97% for valence, 87.73% for dominance, and 90.84% for liking.
A novel deep learning architecture combining an autoencoder, CNN, and LSTM achieved high accuracy in EEG-based emotion recognition.
Emotions are caused by a human brain reaction to objective events. The purpose of this study is to investigate emotion identification by machine learning using electroencephalography (EEG) data. Current research in EEG-based emotion recognition faces significant challenges due to the high-dimensionality and variability of EEG signals, which complicate accurate classification. Traditional methods often struggle to extract relevant features from noisy and high-dimensional data, and they typically fail to capture the complex temporal dependencies within EEG signals. Recent progress in machine learning by deep neural networks has opened up opportunities to develop methods highly efficient and practicable as to serve useful real-world applications. The purpose of this study is to investigate a novel end-to-end deep learning method of emotion recognition using EEG data, which prefaces a combination of two-dimensional (2D) convolutional network (CNN) and Long short-term memory network (LSTM) by an autoencoder. The autoencoder layers seek a lower dimensionality encoding for optimal input signal reconstruction, and the 2D CNN/LSTM combination layers capture both spatial and temporal features that best describe the emotion classes present in the data. Experiments in four-category classification of emotions, using the public and freely available DEAP dataset, revealed that the method reached superior performance: 90.04% for the "arousal" category, 89.97% for "valence", 87.73% for "dominance," and 90.84% for liking", as measured by the accuracy metric.
Henni et al. (Wed,) conducted a other in Emotion recognition (n=32). Autoencoder combined with 2D CNN and LSTM vs. Ablation models and previous state-of-the-art models was evaluated on Emotion classification accuracy. A novel deep learning architecture combining an autoencoder, 2D CNN, and LSTM achieved emotion classification accuracies of 90.04% for arousal, 89.97% for valence, 87.73% for dominance, and 90.84% for liking.