Affective EEG-Based Person Identification Using the Deep Learning Approach

TL;DR

This study introduces a deep learning cascade combining CNNs and RNNs for affective EEG-based person identification, achieving near-perfect accuracy across diverse emotional states and reducing electrode count for practical use.

Contribution

It proposes a novel CNN-RNN cascade model for affective EEG-based person identification, outperforming traditional methods and demonstrating robustness across emotional states with fewer electrodes.

Findings

Achieves up to 100% CRR on DEAP dataset.

CNN-GRU slightly outperforms CNN-LSTM in accuracy and speed.

Reduces electrode count to five with high accuracy.

Abstract

Electroencephalography (EEG) is another mode for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEG-based PI using a deep learning approach. \textcolor{red}{We proposed a cascade of deep learning 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. \textcolor{red}{We evaluated two types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent Unit (CNN-GRU). } The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90--100\% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100\% mean \emph{CRR} comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17\% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time. \textcolor{red}{Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works.