Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification

TL;DR

This paper presents a machine learning approach to classify EEG signals for biometric identification, demonstrating high accuracy with consumer-grade devices and potential applications in VR/AR and healthcare.

Contribution

It introduces an advanced machine learning pipeline that reliably classifies EEG data for individual identification using inexpensive sensors, even with EEG novices.

Findings

Achieved about 85% validation accuracy in noise classification.

Achieved about 80% validation accuracy in participant identification.

Effective with low-cost EEG devices and untrained users.

Abstract

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal parameters found that elicit a steady-state visually evoked potential (SSVEP) in certain parts of the cerebral cortex where it can be reliably perceived by the electrode of the EEG device. After that, we continue with a description of the advanced machine learning pipeline model that can perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise (about 85% validation score) and (b) reliably distinguish between EEG records collected from different human participants (about 80% validation score). Finally, we demonstrate that the proposed method works reliably even with an inexpensive (less than $100) consumer-grade EEG sensing device and with participants who do not have previous experience with EEG technology (EEG illiterate). All this in combination opens up broad prospects for the development of new types of consumer devices, [e.g.] based on virtual reality helmets or augmented reality glasses where EEG sensor can be easily integrated. The proposed method can be used to improve an online user experience by providing [e.g.] password-less user identification for VR / AR applications. It can also find a more advanced application in intensive care units where collected EEG data can be used to classify the level of conscious awareness of patients during anesthesia or to automatically detect hardware failures by classifying the input signal as noise.