Convolutional Neural Network for Closed-Set Identification from Resting State Electroencephalography

Abstract

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG signals. A new architecture for a convolutional neural network (CNN) that uses EEG signals is suggested in this paper for biometric identification. A CNN does not need complex signal pre-processing, feature extraction, and feature selection stages. The EEG datasets utilized in this research are the resting state eyes open (REO) and the resting state eyes closed (REC) EEG. Extensive experiments were performed to design this deep CNN architecture. These experiments showed that a CNN architecture with eleven layers (eight convolutional layers, one average pooling layer, and two fully connected layers) with an Adam optimizer resulted in the highest accuracy. The CNN architecture proposed here was compared to existing models for biometrics using the same dataset. The results show that the proposed method outperforms the other task-free paradigm CNN biometric identification models, with an identification accuracy of 98.54%.

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

“…Physionet data set used by the ref. [4][5][6], [11], [17], [20], [23], [25], [27][28], [33], and [37][38][39] and work on the publicly available dataset consisting of EEG of 109 participants completing various motor/imagery duties, it's a popular benchmark for biometric with EEG. In [9] datasets from four different experiments measuring endogenous brain functions (driving fatigue and emotion) in addition to time-locked artificially created brain responses from 157 subjects, [5] datasets including emotion and combined data.…”

“…Additionally, the signals recorded by EEG systems must be carefully analyzed and interpreted to get useful data regarding the brain's electrical activity. In [2], [4][5][6], [9], [11], [14], [16][17][18][19], [20], [23], [25], [27][28], [30], [33], and [37][38][39] worked on BCI2000 system to record and analyzed EEG using 32 electrodes, while [1][8] [34][40] used AgCl electrodes EEG signals were recorded using a (Bio semi) Active Two system, EEG data were collected at a 512 sampling rate (Hz), AgCl with 32 electrodes works on the (10-20) of the international systems. Another device was using named GALILEO BE Light amplifier equipped with 19 channels/electrodes.…”

“…The design was tested on two baseline scenarios in (RS), (EC, and EO) as shown in [4], [14], [20], [25], [28], [36][37][38][39]. in [10] EC Use this trigger only with the data to build the model and extract the results.…”

“…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.…”