Bi-hemisphere asymmetric attention network: recognizing emotion from EEG signals based on the transformer

Zhong, Xinyue; Gu, Yun; Luo, Yutong; Zeng, Xiaomei; Liu, Guangyuan

doi:10.1007/s10489-022-04228-2

Cited by 18 publications

(3 citation statements)

References 53 publications

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“…represents the instantaneous phase difference related to the coordinate of the lead at time τ . The expression of instantaneous phase difference for the current channel of the W -th sample is equation (6).…”

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confidence: 99%

“…The gradual development of BCI has led to its increasing application in evaluating, enhancing, assisting, replacing, and restoring the cognitive and sensory motor functions of people with disabilities. In addition, the continuous progress of bio-sensing technology has improved the recognition rate of EEG and promoted the rapid conversion of BCI [6].]. However, BCI technology, which integrates multiple disciplines, is still in its early stages.…”

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confidence: 99%

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Analysis of EEG Signals Fused With CNN and Complex Networks

Ma,

2024

IEEE Access

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Currently, brain-computer interface technology still poses hidden dangers in the complete control of ideas. Additionally, there are issues with the low sampling frequency and accuracy of EEG signal acquisition equipment. To address these concerns, this study proposes a combined model for EEG signal recognition and classification analysis by combining frequency division multi-feature complex brain networks with parallel convolutional neural networks. The effectiveness of this model has been verified. In model visualization analysis, the visualization results of t-distribution random neighborhood embedding in the third separable convolutional layer indicate that the two types of imagination have already experienced separation. There is a clear boundary between the two at position 0 on both the horizontal and vertical axes. This is a significant improvement compared to the comparative model. In the model performance verification, the full band classification accuracy in the synchronous network was maintained between 60% to 84%, and the μ-rhythm was maintained between 59% to 81%. The average classification accuracy of the combined model was 77.40% with higher performance, which was higher than 68.53% and 70.87% of the single scale convolutional neural network. In comparison with deep learning algorithms, the average classification accuracy of the combined model was 85.74%, much higher than the 66.20% and 76.69% of the comparative models. The composite model constructed has good performance in recognizing and classifying electroencephalogram signals. It can be effectively applied in practical brain-computer interface technology or electroencephalogram signal analysis.

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Analysis of EEG Signals Fused With CNN and Complex Networks

Ma,

2024

IEEE Access

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“…When experiencing emotions, the brain's left and right hemispheres are involved in different ways [4]. For example, it has been shown that the left prefrontal area is more involved in dependent emotional reactions and the right prefrontal area is involved in withdrawal reactions [5]. Generally, it is hypothesized that the left hemisphere is dominant in positive emotions, and the right hemisphere is dominant in negative emotions [6].…”

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confidence: 99%

Using EEG Effective Connectivity Based on Granger Causality and Directed Transfer Function for Emotion Recognition

Wang,

Sun

2023

IJACSA

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Emotion is a complex phenomenon that originates from everyday issues and has significant effects on individual decisions. Electroencephalography (EEG) is one of the widely used tools in examining the neural correlates of emotions. In this research, two concepts of Granger causality and directional transfer function were utilized to analyze EEG data recorded from 36 healthy volunteers in positive, negative and neutral emotional states and determine the effective connectivity between different brain sources (obtained through independent component analysis). Shannon entropy was utilized to sort the brain sources obtained by the ICA method, and average topography helps to add spatial information to the proposed connectivity models. According to the obtained confusion matrix, our method yielded an overall accuracy of 75% in recognizing three emotional states. Positive emotion was recognized with the highest accuracy of 87.96% (precision = 0.78, recall = 0.78 and F1-score = 0.81), followed by neutral (accuracy = 82.41%) and negative (accuracy = 79.63%) emotions. Indeed, our proposed method achieved the highest recognition accuracy for positive emotion. The proposed model in the present study has the ability to identify emotions in a completely personalized way based on neurobiological data. In the future, the proposed approach in the present study can be integrated with machine learning and neural network methods.

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From gram to attention matrices: a monotonicity constrained method for eeg-based emotion classification

Kuang

Michoski

et al. 2023

Appl Intell

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Bi-hemisphere asymmetric attention network: recognizing emotion from EEG signals based on the transformer

Cited by 18 publications

References 53 publications

Analysis of EEG Signals Fused With CNN and Complex Networks

Analysis of EEG Signals Fused With CNN and Complex Networks

Using EEG Effective Connectivity Based on Granger Causality and Directed Transfer Function for Emotion Recognition

From gram to attention matrices: a monotonicity constrained method for eeg-based emotion classification

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