Subjects identification using EEG-recorded imagined speech

Moctezuma, Luis Alfredo; Torres-García, Alejandro A.; Villaseñor-Pineda, Luis; Carrillo, Maya

doi:10.1016/j.eswa.2018.10.004

Cited by 30 publications

(36 citation statements)

References 8 publications

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“…In our previous works, we have studied and compared various paradigms, i.e. resting-state potentials and ERPs, using various types of electrodes, a various number of channels, and channel localization [2][3][4]6 . Several parameters are yet to be optimized.…”

Section: Discussionmentioning

confidence: 99%

“…We tested a multi-class SVM, which provides a global solution. The classification complexity does not depend on the dimensionality of the feature space and the sensitivity to the number of features is relatively low 25 , as the necessary time to create a model is O N ( ) 3 , where N is the length of the feature vector, and…”

Section: Datasetmentioning

confidence: 99%

“…A number of different paradigms are used to stimulate and record EEG signals in various subject identification approaches: imagined speech [1][2][3] , resting-state potentials 4,5 , and event-related potentials (ERPs) 6 . ERPs are very low-voltage signals emitted by the human brain that appears on the scalp in response to specific events or stimuli.…”

mentioning

confidence: 99%

“…An important element is dimensionality reduction, which can be tackled through channel selection and feature extraction. Several approaches can be used to accomplish this task, including those based on methods such as principal component analysis (PCA) 13 , discrete wavelet transform (DWT) 3 , empirical mode decomposition (EMD) 2,3,6 , and even approaches using raw data as input for different configurations of neural networks (NN) 14,15 . Based on the current state-of-the-art and the results of our previous studies, we used EMD for sub-band extraction and then extracted four features for each: instantaneous and Teager energy and Higuchi and Petrosian fractal dimensions.…”

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confidence: 99%

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Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

Moctezuma

Molinas

2020

Sci Rep

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We present a four-objective optimization method for optimal electroencephalographic (eeG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two subbands, extracted using empirical mode decomposition (eMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels. The optimization process was performed by the non-dominated sorting genetic algorithm (NSGA), which found a threechannel combination that achieved an accuracy of 0.83, with both a true acceptance rate (TAR) and a true rejection rate (TRR) of 1.00. In the best case, we obtained an accuracy of up to 0.98 for subject identification with a TAR of 0.95 and a TRR 0.93, all using seven EEG channels found by NSGA-III in a subset of subjects manually created. The findings were also validated using 10 different subdivisions of subjects randomly created, obtaining up to 0.97 ± 0.02 of accuracy, a TAR of 0.81 ± 0.12 and TRR of 0.85 ± 0.10 using eight channels found by NSGA-III. These results support further studies on larger datasets for potential applications of EEG in identification and authentication systems.

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Section: Discussionmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

Moctezuma

Molinas

2020

Sci Rep

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“…For EEG-based biometric systems, several approaches have been presented using various paradigms to stimulate and record the EEG signals, i.e. imagined speech [1][2][3] , resting-state [4][5][6][7][8][9][10] , and event-related potentials (ERPs) 11,12 .…”

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confidence: 99%

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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Electroencephalography based imagined alphabets classification using spatial and time‐domain features

Agarwal

Kumar

2021

Int J Imaging Syst Tech

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Imagined speech is a neuro-paradigm that can provide an alternative communication channel for patients in a locked-in syndrome state. We have performed an experiment in which a 32 channel industry-standard electroencephalography (EEG) device was used to record 26 imagined English alphabets from 13 subjects. We denoised the imagined signals by discrete wavelet transform and extracted the spatial filters by common spatial pattern method, and time-domain features. Spatial features when classified with linear support vector machine, and time-domain features classified by random forest gave the best results. Alpha, beta, and theta bands could classify imagined alphabets better than other bands and had average classification accuracies of 88.59%, 87.39%, and 88.97%, respectively by using spatial features and 81.88%, 76.72%, and 79.25%, respectively, by time-domain features. The grand average accuracies of all the 26 alphabets in six EEG frequency bands was found to be 77.97% in a subject independent binary classification framework.

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Subjects identification using EEG-recorded imagined speech

Cited by 30 publications

References 8 publications

Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

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

Electroencephalography based imagined alphabets classification using spatial and time‐domain features

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