Affective EEG-Based Person Identification Using the Deep Learning Approach

There are several reports available on affective electroencephalography-based personal identification (affective EEG-based PI), one of which uses a small dataset and another reaching less than 90% of the mean correct recognition rate \emph{CRR},. Thus, the aim of this paper is to improve and evaluate the performance of affective EEG-based PI using a deep learning approach. The state-of-the-art EEG dataset DEAP was used as the standard for affective recognition. Thirty-two healthy participants participated in the experiment. They were asked to watch affective elicited music videos and score subjective ratings for forty video clips during the EEG measurement. An EEG amplifier with thirty-two electrodes was used to record affective EEG measurements from the participants. To identify personal EEG, a cascade of deep learning architectures was proposed, using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. There has been a cascade of CNNs, with recurrent models known as Long Short-Term Memory (CNN-LSTM) and Gate Recurrent Unit (CNN-GRU) for comparison. Experimental results indicate that CNN-GRU and CNN-LSTM can deal with an EEG (4--40 Hz) rom different affective states and reach up to 99.90--100% mean \emph{CRR}. On the other hand, a traditional machine learning approach such as a support vector machine (SVM) using power spectral density (PSD) as a feature does not reach 50% mean \emph{CRR}. To reduce the number of EEG electrodes from thirty-two to five for more practical application, $F_{3}$, $F_{4}$, $F_{z}$, $F_{7}$ and $F_{8}$ were found to be the best five electrodes for application in similar scenarios to those in this study. CNN-GRU and CNN-LSTM reached up to 99.17% and 98.23% mean \emph{CRR}, respectively.