Support Vector Machine for Person Classification Using the EEG Signals

TL;DR

This paper demonstrates that EEG signals combined with Support Vector Machine classifiers can achieve high accuracy in individual identification, offering a promising biometric authentication method resistant to spoofing.

Contribution

It introduces a novel EEG-based biometric identification approach using SVM, achieving up to 92.9% accuracy with salient features from EEG data.

Findings

Maximum accuracy of 92.9% with EEG-based SVM classifier

Salient features from EEG signals effectively distinguish individuals

EEG-based biometrics provide a liveness detection mechanism

Abstract

User authentication is a pivotal element in security systems. Conventional methods including passwords, personal identification numbers, and identification tags are increasingly vulnerable to cyber-attacks. This paper suggests a paradigm shift towards biometric identification technology that leverages unique physiological or behavioral characteristics for user authenticity verification. Nevertheless, biometric solutions like fingerprints, iris patterns, facial and voice recognition are also susceptible to forgery and deception. We propose using Electroencephalogram (EEG) signals for individual identification to address this challenge. Derived from unique brain activities, these signals offer promising authentication potential and provide a novel means for liveness detection, thereby mitigating spoofing attacks. This study employs a public dataset initially compiled for fatigue analysis, featuring EEG data from 12 subjects recorded via an eight-channel OpenBCI helmet. This dataset extracts salient features from the EEG signals and trains a supervised multiclass Support Vector Machine classifier. Upon evaluation, the classifier model achieves a maximum accuracy of 92.9\%, leveraging ten features from each channel. Collectively, these findings highlight the viability of machine learning in implementing real-world, EEG-based biometric identification systems, thereby advancing user authentication technology.