Combination of EOG and EEG for emotion recognition over different window sizes

Cai, Huili; Liu, Xiaofeng; Jiang, Aimin; Ni, Rongrong; Zhou, Xiaoqing; Cangelosi, Angelo

doi:10.1109/ichms53169.2021.9582628

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…These signals are susceptible to external interference and easily induce noises during signal acquisition, resulting low recognition accuracy. The second type is through interpreting physiological signals from the brain, heart, and eyes using electroencephalogram (EEG) [9], electrocardiogram (ECG) [10], electrooculogram(EOG) [11], galvanic skin response (GSR) [12] and so on. These signals are spontaneously generated, not easily controlled, and therefore have been widely studied in emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised EEG Emotion Recognition Models Based on CNN

Wang

Cammon

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.

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Section: Introductionmentioning

confidence: 99%

Self-Supervised EEG Emotion Recognition Models Based on CNN

Wang

Cammon

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…• We propose a module(L-RNN) for extracting serial EEG temporal features from local brain regions, which is used to extract features of emotional state differences between left and right brain regions and learn the differences between brain regions under different emotions. [23].…”

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confidence: 99%

MASTF-net: An EEG Emotion Recognition Network Based on Multi-Source Domain Adaptive Method Based on Spatio-Temporal Image and Frequency Domain Information

Xu,

Pei,

Han

et al. 2024

IEEE Access

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In the field of neuroscience, the electroencephalogram (EEG) is a crucial indicator of emotion. The EEG emotion recognition method based on domain adaptation (DA) has good objectivity and high time resolution and is the preferred method to study the brain's response to emotional stimuli. However, due to the obvious instability of EEG emotion characteristics, it is difficult to predict the emotion corresponding to EEG signals of cross-subjects by a model that combines all source domains into a single source. In order to solve the problem of cross-subject emotion analysis, we propose an EEG emotion recognition net with a crosssubject multi-source adaptive method (MASTF-net), where EEG features of different subjects are regarded as different domains. Through analyzing the invariance of the target domain and the uniqueness of the source domain, this method realizes the emotional analysis of different objects according to the spatio-temporal images and frequency domain information. First, features of EEG image are extracted from frequency and time dimensions. Secondly, combined with the serialized EEG frequency characteristics of local brain regions, independent classification module are established for different domains to recognize the emotion feature distribution of different subjects. In addition, a feature extraction method of differential entropy(DE) data of EEG is proposed based on frequency band division, which can provide stable feature input for our network structure. Finally, experiments are conducted on the SEED dataset. The experimental results show that our method has better classification accuracy in the experiment on the problem of cross multiple subjects. MASTF-net is superior to other relevant methods and models in multi-source domain. On the issue of cross subject emotion analysis, the highest accuracy of our method can reach 88.19%.

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