Based On EEG-NIRS Emotion Regulation Mechanism And Brain-computer Interface Application Research

In the development of human psychology,a series of problems such as the generation and regulation of emotions have always been a hot issue in psychological research.In the study of Brain Computer Interface(BCI),how to find high-precision brain signal characteristics and reduce the impact of the test's own factors on the accuracy of experiments is a problem that BCI researchers have been exploring.The study of this emotion and brain-computer interface is also an exploration of human brain.In the process of exploration,the combination of two modern neuroimaging imaging methods,Electroencephalogram(EGG)and Near-Infrared Spectroscopy(NIRS),can provide us with more valuable results.Based on the existing emotional research,this paper studies the mechanism of emotional regulation through the fusion of EEG and NIRS.In the pre-experimental stage,15 subjects were selected to establish an emotionally induced material library,and three emotion-induced experiments of video,audio and self-imagination were designed.The power spectrum energy of the ? rhythm and ? rhythm of the subject was used as the training feature,and the results were learned and classified using Support Vector Machine(SVM),and the three kinds of “sadness”,“happy” and “fear” were obtained.The classification accuracy of emotions was 70.8%,83.3% and 76.4%.In the emotional adjustment experiment,another 15 subjects were selected to complete the task of adjusting fear in different situations after the video induced fear.We use the Fractal Brownian Motion(fBM)to calculate the Hurst index of the frontal channel and find that the changes in EEG activity caused by fear are waning,and the process of fear regulation leads to ? and ? rhythms.The energy is reduced,but if the environment in which the subject is tested changes,the rate of decrease will be affected.In the applied research of BCI,we analyzed the data of multi-robot collaborative task controlled by 20 subjects with Steady state visual evoked potential(SSVEP),and obtained the average accuracy of binocular and dominant monocular SSVEP control.The accuracy was 82.85% and 87.40%,respectively,and the accuracy decreased with the increase of experimental time.This study shows that the accuracy of the traditional SSVEP stimulation paradigm long-term monocular SSVEP machine-to-machine cooperative task control can meet the basic control requirements,and the difference in visual acuity between the two eyes will reduce the accuracy of SSVEP control.At the same time,we also conducted a preliminary study on the Motor Imaging BCI based on EEG-NIRS fusion.The results show that the ? rhythm of EEG combined with the change of oxygenated hemoglobin concentration(HbO)of NIRS in the exercise zone can be used as the classification feature of BCI.