Criminal psychological emotion recognition based on deep learning and EEG signals


The difficulty of criminal psychological recognition is that it is difficult to classify emotions, and the accuracy of traditional recognition methods is insufficient. Therefore, it is necessary to improve the accuracy rate in combination with modern computer technology. This study uses deep learning as technical support and combines EEG computer signals to classify criminal psychological emotions. Moreover, a method for classifying EEG signals based on the state of mind of neural networks was constructed in the study. In addition, the EEG is denoised preprocessed by time-domain regression method, and features of the EEG signal parameters of different criminal psychological tasks are extracted and used as the input of the neural network. Finally, in order to verify the effectiveness of the algorithm, a simulation experiment is designed to study the effectiveness of the algorithm. The results show that the method proposed in this paper has certain practical effects.

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The study was supported by the National Key R&D Program of China (Grant No. 2017YFC0820200).

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Correspondence to Qi Liu.

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Liu, Q., Liu, H. Criminal psychological emotion recognition based on deep learning and EEG signals. Neural Comput & Applic 33, 433–447 (2021).

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  • Deep learning
  • Neural network
  • EEG
  • EEG signals
  • Criminal psychology
  • Emotion recognition