Space-CNN: a decision classification method based on EEG signals from different brain regions

Xue, Huang; Yang, Jingmin; Zhang, Wenjie; Yang, Bokai

doi:10.1007/s11517-023-02954-4

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Emotion recognition based on electroencephalogram (EEG) signals has attracted widespread attention, human emotional activity is not constant in the whole brain, However, different regions of the brain do not affect the classification of design decisions equally, and the experimental results suggest that the extent of different brain regions is parietal> frontal> occipital> temporal. [4] Electroencephalogram (EEG) signals are widely used in medicine, affective computing, and other related fields. [3] Using EEG signals, key emotional information can be obtained to make the judgment of anxiety more accurate, and the effect of emotional improvement can be improved by combining with virtual reality exposure therapy.…”

Section: Fig 1 a System Of Virtual Reality Exposure Therapymentioning

confidence: 99%

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

Li,

Wang

2024

Symp Digest of Tech Papers

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Virtual reality (VR) is an immersive technology that can be used to generate scenes of emotional responses. For people with psychological problems, negative emotions can be treated with the help of virtual reality exposure therapy (VRET). EEG signals can more accurately reflect the types of emotions, make up for the lack of emotion recognition, make the treatment more targeted, and the intervention treatment using virtual environment combined with the accurate recognition of emotions by EEG signals can more effectively achieve the therapeutic effect. Therefore, this paper comprehensively explores the research progress of EEG emotion recognition and exposure therapy, studies new psychotherapy regimens carried out in cooperation with therapists, and identifies some key technologies to create a more commercially effective VRET treatment.

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Section: Fig 1 a System Of Virtual Reality Exposure Therapymentioning

confidence: 99%

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

Li,

Wang

2024

Symp Digest of Tech Papers

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GAN-ACNN: a design decision-making algorithm based on EEG signals from different brain regions

Yang,

Xue,

et al. 2024

Eng. Res. Express

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Decision-making is an integral part of an individual's life. Due to the small amount of data in the art design decision dataset, we increase the amount of data using data augmentation. However, different brain regions have distinct effects on the accuracy of art design decisions, so we divide the brain into four areas and investigate the influence of each on the results. We collect electroencephalogram (EEG) data from 16 subjects, apply a band-pass filter to filter it, and then feed it into a generative adversarial network (GAN) for data augmentation. The augmented EEG data is input to a convolutional neural network with a self-attention mechanism (ACNN). The experimental results show that the degree of influence of different brain regions is: right frontal lobe > right parietal-temporal-occipital lobe > left frontal lobe > left parietal-temporal-occipital lobe. In view of this, we find the six optimal channels for art design decisions, and their prediction results are better than those of all channels. In addition, our GAN-ACNN model achieves an average accuracy of 93.51%, which is an effective method. Finally, we also classify the DEAP dataset to evaluate the robustness of the model.

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Space-CNN: a decision classification method based on EEG signals from different brain regions

Cited by 2 publications

References 30 publications

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

GAN-ACNN: a design decision-making algorithm based on EEG signals from different brain regions

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