Comparison of Different Classifiers for Biometric System Based on EEG Signals

Jian-feng, Hu

doi:10.1109/itcs.2010.77

Cited by 9 publications

(6 citation statements)

References 16 publications

(18 reference statements)

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“…In the field of EEG biometrics, the use of SVM classifiers has also received attention. Using conventional AR coefficients features for classification, results were presented using three classifiers, namely LDA, back-propagation Neural Network, and SVM in [80]. The reported accuracies, using a small publicly available database with three subjects and using five electrodes, indicate that ANN and SVM provided comparable performance (identification accuracy ranges from 80.8% to 84.0%) and LDA outperformed both of them by more than 5% (89.5%).…”

Section: Kernel Methodsmentioning

confidence: 99%

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Yang

Deravi

2017

IEEE Trans. Human-Mach. Syst.

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Section: Kernel Methodsmentioning

confidence: 99%

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Yang

Deravi

2017

IEEE Trans. Human-Mach. Syst.

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“…Power Spectral Density (PSD) methods [23]- [26], the Autoregressive Model (AR) [27]- [32], Wavelet Transform (WT) [33], [34] and Hilbert-Huang Transform (HHT) [35], [36] are useful for feature extraction. For feature classification, k-Nearest Neighbour (k-NN) algorithms [37], [38], Linear Discriminant Analysis (LDA) [39], [40], Artificial Neural Networks (ANNs) with a single hidden layer [23], [41]- [43] and kernel methods [44], [45] are popular techniques. In this study, we propose a DL technique to perform both the feature extraction and classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Affective EEG-Based Person Identification Using the Deep Learning Approach

Wilaiprasitporn¹,

Ditthapron

Matchaparn

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

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Electroencephalography (EEG) is another mode for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEGbased PI using a deep learning approach. We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent Unit (CNN-GRU). The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90-100% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100% mean CRR comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time. Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works.

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“… 10 channels DEAP database PCA RBF-SVM KNN ANNs Accuracy = 91.2 [ 52 ] 2018 MI 9 subj. 2 channels BCI Competition IV DWT NB KNN LDA Accuracy = 73 [ 300 ] 2010 MI 6 channels N/A Statistics LDA BPNN SVM Accuracy = 88.6 [ 162 ] 2019 ER 32 subj. 32 channels DEAP database Entropy RF RBF-SVM LDA KNN Accuracy = 90 [ 301 ] 2016 MWL 20 subj.…”

Section: Table A1mentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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Comparison of Different Classifiers for Biometric System Based on EEG Signals

Cited by 9 publications

References 16 publications

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Affective EEG-Based Person Identification Using the Deep Learning Approach

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

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