Cross-Subject Emotion Recognition Brain–Computer Interface Based on fNIRS and DBJNet

Xiaopeng Si · He Huang · Jiayue Yu

73 citations0/0 files storedCyborg and Bionic Systems

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Optical Imaging and Spectroscopy Techniques

Abstract

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.

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References

2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2022TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition 2022Investigating EEG-based functional connectivity patterns for multimodal emotion recognition 2019Spontaneous Speech Emotion Recognition Using Multiscale Deep Convolutional LSTM 2019fNIRS Evidence for Recognizably Different Positive Emotions 2012Automatic detection of a prefrontal cortical response to emotionally rated music using multi-channel near-infrared spectroscopy

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2024EEGMatch: Learning With Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition 2024A bidirectional cross-modal transformer representation learning model for EEG-fNIRS multimodal affective BCI 2024EEG–fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network

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