EEG-Based Biometric Authentication: Advancing Security Through Motor Imagery and Deep Learning

EEG signals, reflecting synaptic activity across the brain's surface, offer a promising avenue for biometric authentication due to their resilience to spoofing attacks and immunity to coercion. This study leverages the BCI IV 2a dataset, where participants imagine movements of four body parts: right hand, left hand, both feet, and tongue. A two-dimensional CNN with 6 convolutional layers combined with the self-distillation technique was employed for classification. Various scenarios were analyzed based on task type, signal length, frequency band, and the number of EEG channels. Optimal results were achieved using a combination of frequency bands and the maximum number of channels, with a 4-second signal input yielding 100% accuracy and a 2-second input achieving 99.8397% accuracy for left-hand or tongue movement tasks. Despite these advancements, EEG-based authentication still faces challenges requiring further research to match the reliability and security of traditional biometric methods like fingerprint authentication.