DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

Abstract

In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems. In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed. The performance of the proposed method is experimentally evaluated through the emotional EEG data. The conducted experiment shows that the proposed method approaches the stunning accuracy (ACC) of 99.7% on average and can rapidly train and update the DE-CNN model. Then, the effects of different emotions and the impact of different time intervals on the identification performance are investigated. Obtained results show that different emotions affect the identification accuracy, where the negative and neutral mood EEG has a better robustness than positive emotions. For a video signal as the EEG stimulant, it is found that the proposed method with 0–75 Hz is more robust than a single band, while the 15–32 Hz band presents overfitting and reduces the accuracy of the cross-emotion test. It is concluded that time interval reduces the accuracy and the 15–32 Hz band has the best compatibility in terms of the attenuation.

Global research on artificial intelligence-enhanced human electroencephalogram analysis

“…Furthermore, ensemble classifiers are more effective than a single strong learner [238]. Thus, it is suggested that scholars should Second, machine learning and deep learning have been widely applied to EEG data analysis, particularly CNNs (e.g., [239][240][241][242][243][244][245]). CNNs are effective as function approximators and are commonly used to resolve classification issues involving EEG signal decoding for BCIs.…”

“…Hence, we can see that application of ANNs is relatively limited in the analysis of EEG data compared to CNNs. The application of CNNs in EEG studies includes intuitive robotic arm control [239], decode motor preparation of upper limbs [240], identity recognition [241], emotion recognition [242], decoding human brain activity [243], EEG motor imagery classification [244], and decoding EEG four-class motor imagery tasks [245].…”