Nikolopoulos M. et al.

Submitted to 10th Graz Brain-Computer Interface Conference 2026.

Abstract

A growing area of brain computer interface (BCI) research focuses on user identification, where individual neural signatures support secure authentication and personalization. Among these, visual evoked potentials (VEPs) are widely used due to their reliability and robustness, yet the integration of multiple neural modalities for identification remains underexplored. This study investigated, for the first time, the combined use of movement-related cortical potentials (MRCPs) and VEP features for electroencephalography (EEG)- based identification of individuals with spinal cord injury (SCI). We analyzed a publicly available EEG dataset from ten participants with cervical SCI who attempted five hand movements: pronation, supination, palmar grasp, lateral grasp, and hand open. VEPs were elicited by visual cues instructing the movement attempts, while MRCPs reflected the motor preparation and execution processes. Classification results showed that the “hand open” movement achieved the highest identification accuracy (40.28%, 4.03 × above the 10% chance level). These findings demonstrate that combining MRCP and cue-evoked VEP features offers a promising multimodal biometric approach for identifying individuals with SCI.

Panteli, A., Kalaitzi, E., & Fidas, C. A. (2025). Identifying Individual Information Processing Styles During Advertisement Viewing Through EEG-Driven Classifiers. Information, 16(9), 757. https://doi.org/10.3390/info16090757

This article belongs to the Special Issue Human–Computer Interaction in Marketing: Emerging Interfaces, Cognitive Engagement, and Child–Computer Interaction

K. Kyriaki and C. A. Fidas, "A Human–Computer-Interaction Exploration of Working Memory Using EEG," in IEEE Access, vol. 13, pp. 157877-157908, 2025, doi: 10.1109/ACCESS.2025.3606802.

Published in: IEEE Access ( Volume: 13)