In recent years, deep learning has been widely used in emotion recognition, but the models and algorithms in practical applications still have much room for improvement. With the development of graph convolutional neural networks, new ideas for emotional recognition based on EEG have arisen. In this paper, we propose a novel deep learning model-based emotion recognition method. First, the EEG signal is spatially filtered by using the common spatial pattern (CSP), and the filtered signal is converted into a time–frequency map by continuous wavelet transform (CWT). This is used as the input data of the network; then the feature extraction and classification are performed by the deep learning model. We called this model CNN-BiLSTM-MHSA, which consists of a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and multi-head self-attention (MHSA). This network is capable of learning the time series and spatial information of EEG emotion signals in depth, smoothing EEG signals and extracting deep features with CNN, learning emotion information of future and past time series with BiLSTM, and improving recognition accuracy with MHSA by reassigning weights to emotion features. Finally, we conducted experiments on the DEAP dataset for sentiment classification, and the experimental results showed that the method has better results than the existing classification. The accuracy of high and low valence, arousal, dominance, and liking state recognition is 98.10%, and the accuracy of four classifications of high and low valence-arousal recognition is 89.33%.
EEG signals classification plays a crucial role in motor imagery brain computer interface systems. Traditional convolutional neural networks tend to ignore temporal information when classifying motor imagery EEG signals, it uses a single-scale convolutional kernel, resulting in poor classification performance. In this paper, we propose a parallel fusion algorithm based on dual attentional multi-scale convolutional neural networks (DAMSCN) and long and short-term memory (LSTM). Firstly, DAMSCN uses convolutional kernels of different sizes at the same layer to extract time-frequency features of EEG signals at different scales, and introduces a dual attention mechanism. At the same time, LSTM extracts temporal features from the EEG signals. Then, the fusion and classification of all features is achieved with the help of fully connected layers and softmax layers. Finally, experiments are conducted on domain-specific public dataset to verify the performance of the algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.