Convolutional Network for EEG-Based Biometric

Schons, Thiago; Moreira, Gladston; Silva, Pedro H. L.; Coelho, Vitor Nazário; Luz, Eduardo

doi:10.1007/978-3-319-75193-1_72

Cited by 29 publications

(39 citation statements)

References 8 publications

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“…al. [5] have also proposed the use of CNNs for EEG-based biometric using eyes open EEG signals for training and 5 eyes close 12s-EEG segments for testing. The proposed CNN architecture achieved 0.19% Equal Error Rate (EER), however, in practice, collecting 12s EEG signals could take too long for authentication purposes.…”

Section: B Deep Learning For User Identificationmentioning

confidence: 99%

EEG-based user identification system using 1D-convolutional long short-term memory neural networks

Sun

2019

Expert Systems with Applications

148

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Electroencephalographic signals (EEG) have widely used in medical applications, yet the use of the signals as biometric identifier has only gained interests in the last few years. The advantage of EEG-based biometric recognition lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. A novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based biometric identification is proposed in this paper. The complementarity of CNNs and LSTMs largely improves the performance of the identification by removing variation of the input, facilitating the temporal modeling and transforming the features into the high dimensional space which makes the classification more effective respectively. We investigate the architecture to determine the appropriate topology to improve the efficacy of the 1D-Convolutional LSTMs for the authentication. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high recognition rate of 99.58%, when using only 16 channel EEG signals, which outperforms the state-of-the-art EEG-based biometric identification methods.

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Section: B Deep Learning For User Identificationmentioning

confidence: 99%

EEG-based user identification system using 1D-convolutional long short-term memory neural networks

Sun

2019

Expert Systems with Applications

148

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“…EEG recordings from three different sessions of 50 subjects were employed for analyzing its repeatable characteristics. Deep convolution neural networks have also been used for EEG biometrics [15]. Using the open-source database (Physionet EEG Database), an Equal Error Rate (EER) of 0.19% was reported.…”

Section: Introductionmentioning

confidence: 99%

Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition

2019

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This paper introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The feature extraction method is based on the wavelet transform of the raw EEG signal. Furthermore, the logarithms of wavelet coefficients are processed using the discrete cosine transform (DCT). The DCT coefficients from each wavelet band are used to form the feature vectors for classification. As an application in the biometrics scenario, the effectiveness of the electrode locations on person recognition is also investigated, and suggestions are made for electrode positioning to improve performance. The effectiveness of the proposed feature was investigated in both identification and verification scenarios. The identification results of 98.24% and 93.28% were obtained using the EEG Motor movement/imagery dataset (MM/I) and the UCI EEG database dataset, respectively, which compares favorably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario, when compared with some similar systems from the published literature. A multi-session analysis is simulated using with eyes open and eyes closed recordings from the MM/I database. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis. INDEX TERMS Biometrics, feature extraction, EEG.

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“…Several approaches have been proposed for the creation of a biometric system following various experiment configurations, with various paradigms and methods for feature extraction and classification using the public EEG Motor Movement/Imagery Dataset (EEGMMIDB), using various configurations of neural networks 14,[18][19][20] , other supervised and unsupervised techniques 5,[21][22][23][24][25][26][27][28][29][30][31] , and methods for EEG channel selection 6,32,33 .…”

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confidence: 99%

“…In the literature, researchers report from simple NN structures (i.e., a single hidden layer) to more complex networks (Recurrent and CNN), but it requires the improvement of computational power, with faster CPUs and the use of GPUs 15,[28][29][30][31]34 . To overcome the need for a large amount of data of deep learning approaches, there is an approach that uses simple data augmentation techniques by creating overlapped time windows 18 . Other related proposals using neural networks have been presented and compared in the state of the art [28][29][30][31] , where some of the most relevant works used around 100 subjects and in most of the cases 64 channels for testing their approaches 13,14,18,36 .…”

mentioning

confidence: 99%

“…To overcome the need for a large amount of data of deep learning approaches, there is an approach that uses simple data augmentation techniques by creating overlapped time windows 18 . Other related proposals using neural networks have been presented and compared in the state of the art [28][29][30][31] , where some of the most relevant works used around 100 subjects and in most of the cases 64 channels for testing their approaches 13,14,18,36 . However, there is no defined method for channel selection, since the process for selecting the most relevant channels requires the repetition of the classification process several times, and it is well known that deep learning approaches are computationally costly 30,34 .…”

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confidence: 99%

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Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

Moctezuma

Molinas

2020

Sci Rep

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We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR). This method was tested on EEG signals from 109 subjects of the public motor movement/imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. We were able to obtain a TAR of $$1.000 \pm 0.000$$ 1.000 ± 0.000 and TRR of $$0.998 \pm 0.001$$ 0.998 ± 0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to $$0.993 \pm 0.01$$ 0.993 ± 0.01 and a TRR of up to $$0.941 \pm 0.002$$ 0.941 ± 0.002 for the Pareto-front, using NSGA-III and DWT-based features in the resting-state with the eyes-open. In the resting-state with the eyes-closed, the TAR was $$0.997 \pm 0.02$$ 0.997 ± 0.02 and the TRR $$0.950 \pm 0.05,$$ 0.950 ± 0.05 , also using DWT-based features from three channels. These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. Furthermore, the candidates obtained throughout the optimization process of NSGA-III showed that it is possible to obtain TARs and TRRs above 0.900 using LOF and DWT- or EMD-based features with only one to three EEG channels, opening the way to testing this approach on bigger datasets to develop a more realistic and usable EEG-based biometric system.

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Convolutional Network for EEG-Based Biometric

Cited by 29 publications

References 8 publications

EEG-based user identification system using 1D-convolutional long short-term memory neural networks

EEG-based user identification system using 1D-convolutional long short-term memory neural networks

Improved Time-Frequency Features and Electrode Placement for EEG-Based Biometric Person Recognition

Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

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