Biometric identification using single channel EEG during relaxed resting state

Abstract

Brain signals have long been studied within various fields like medical, physiotherapy, and neurology for many years. One of the main reasons for this interest is to better understand brain diseases like Parkinson's, Schizophrenia, Alzheimer's, epilepsy, spinal cord injuries, and stroke among others. More recently, they have been used in brain-computer interface systems for rehabilitation, entertainment, and assistance applications. Even with the growing interest in clinical applications, the scientific community has only recently investigated the possibility of using brain signals as a potential biometric feature that can be used in people authentication and recognition systems. In this research, the authors have studied the use of brain signals acquired using electroencephalogram (EEG) during both eyes open and eyes closed states for identification based on a large dataset of 109 subjects. The use of a novel mind relaxation metric to determine the optimum epochs to select for the classification and verification has generated very high classification results, in the range of 97-99% based on a single channel. The approach has also been validated against another dataset to verify its consistency and repeatability. The results demonstrate that it is possible to move towards a single-channel biometric identification system with a very high level of reliability and accuracy.

Evaluation of EEG identification potential using statistical approach and convolutional neural networks

“…This indicates a high variability of the EEG depending on the subject's state (and possibly installation). In previous studies, this aspect has either not been taken into account or has been taken into account indirectly (for example, by normalizing signals to alpha rhythm [8], that does not guarantee their independence from PPS). It is proposed the network to be trained on EEG data recorded by the subject on different days.…”

“…3. Informational value of rhythms in multiclass subject identification under standard conditions in the "norm" state with respect to EEG variability due to changing mounting and a psychophysiological state [8]. The training was carried out based on the EEGs recorded in the "norm" state on one or more days, and testing was carried out by cross-checking for data not used in training (in the "norm" state or other PPS).…”

Towards using Single EEG Channel for Human Identity Verification

“…They used only 20 subjects based on sixteen electrode placements on the scalp according to the 10-20 international system. In [18], authors trying to get an EEG single channel to be used in the authentication. They used the same dataset which was used in this work based on open and closed eye states with 109 subjects, the obtained system accuracy in the range of 97-99%.…”

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

“…In their work, power spectral density (PSD) is concatenated with the entropy features and improves the performance. In the work of Suppiah et al [18], PSD was also extracted from REC and REO EEG as features, and a Fischer linear discriminant classifier was trained to perform identification. In addition, Lee et al [19] also extracted spectral power, maximum power, and frequency of maximum power in the alpha band from the REC EEG in their design of the biometric authentication system.…”