Efficient and Accurate Neural Fingerprints Obtained via Mean Curve Length of High Dimensional Model Representation of EEG Signals

In this study, we propose and evaluate a feature extraction methodology for the purpose of EEG-based person recognition. To this end, the mean curve length (MCL) was employed subsequent to the representation of EEG signals in an orthogonal geometry through High Dimensional Model Representation (HDMR). To analyze the effectiveness of the methodology, we executed it on a standard publicly available EEG dataset containing 109 subjects and acquired from 64 channels for eyes-open (EO) and eyes-closed (EC) resting-state conditions. The proposed feature was evaluated by comparing it to MCL, beta, and gamma band activities. According to the performance results, applying MCL to the output of the HDMR instead of raw data provides superior performances for identification and authentication. The attained results promise a novel simple, fast, and accurate biometric recognition scheme, named HDMRMCL.