Extraction of brainprint by means of autoencoder with attention mechanism.

Authors

Muhammed Esad Oztemel, Ömer Muhammet Soysal

Published in

Frontiers in computational neuroscience. Volume 20. Pages 1737750. Epub Jul 01, 2026.

Abstract

Electroencephalography (EEG) signals provide a unique opportunity for personal identification due to their inherent permanence and uniqueness. However, EEG data are complex, multidimensional, non-stationary, and time-dependent, making feature extraction and classification particularly challenging. This study proposes a framework by integrating autoencoder-based feature extraction with self-attention mechanism for EEG-based personal identification utilizing the proposed EEG data cube spatio-temporal stream representation. Three types of stimuli were applied: auditory, cognitive, and resting state. EEG data were collected in-house from college students in two sessions separated by 10 days. A longitudinal design was employed, with Session-1 data used for training and Session-2 data for testing, ensuring robustness against inter-session variability. Extracted features were subsequently classified using Support Vector Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), and Random Forests (RF). SVM achieved accuracy scores 97.62% when identifying two subject, 82.54% when identifying five subjects and 62.90% when identifying seven subjects using the resting state neural patterns. This research highlights the feasibility of using deep learning networks strengthened with a self-attention mechanism for advancing biometric identification through EEG signals.

PMID:
42460389
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.

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