EEG-Based Person Identification during Escalating Cognitive Load

Abstract

With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

Electroencephalographic (EEG) based Deep Learning (DL): A Comparative Review

“…Other researchers try another mode of acquisition EEG data by Self-Collected (SC) used in them researcher's. We founded a self-collected dataset by the researchers that consist of different numbers of subjects between (8 -50) whose ages range between 15-50 years performed many tasks [7][8][9][10], [12][13][14][15][16][17][18][19], [21][22], [24], [26], and [32]. The ref.…”

“…[17] use different motor/imagery tasks baseline tests (one with EO, other with EC). In [32], accumulation of cognitive brain load from lying down and closing your eyes to the end of a challenging game. In [38] three experiments applied, first experiment in Resting eye open (REO), and second experiment in Resting eye closed (REC), and third experiment (2 min) runs with (4) various (MI) activities.…”

“…These models can learn to identify patterns and relationships in the data and generate predictions or classifications based on these features. In ISSN: 2710-2165 http://doi.org/10.58564/IJSER.1.2.2023.70 https://ijser.aliraqia.edu.iq [1], [4], [6], [8], [14], [18], [20], [22], [25], [28], [32], [35][36][37][38][39] used the (CNNs) models or changing in the structure of the (CNN) [8], to extract features, while [10], [13] apply a model called Siamese CNNs, another researchers use the graph convolutional neural network (GCNN) to detain deep essential structural representations from EEG graphs immediately [5], and in [19] propose an adversarial inference learning with CNN to extend DL models. In [24], [33], used RNN and Long Short-Term Memory (LSTM) which is a special form of RNN architecture with FaceNet to create a new LSTM-RNN model that aided in the extraction of the feature vector for a given 100s EEG signal which leads with a support vector machine (SVM) used as a clustering tool.…”

“…A DL model integrating functional connectivity was proposed for biometric identification in a recent work [16]. In recent studies CNNs have been proposed to EEG-based person identification and authentication, achieving promising results [2], [8][9][10][11][12][13][14][15][16][17][18][19][20], [22], [25], [28], [29], [31], [32] and [34][35][36][37][38][39]. These studies use CNNs model directly on the EEG amplitude fluctuations.…”