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

Abstract

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.

EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization

“…The strong points of this MO algorithm are: (1) the fast non-dominated sorting ranking selection method used to emphasize Pareto-optimal solutions, (2) maintaining the population diversity by using the crowding distance and (3) the elitism approach, which ensures the preservation of best candidates through generations without the setting of any new parameters other than the normal genetic algorithm parameters, such as population size, termination parameter, crossover and mutation probabilities. Additionally, it was often used for the elimination of EEG channels with the similar purpose as in our case-dimensionality reduction [ 48 ]. This paper uses the implementation of NSGA-II provided by the MOEA framework [ 49 ] and is based on the guidelines defined in [ 46 , 50 ].…”

“…All the available information regarding patients is available from the PhysioNet portal [ 47 ], from the 2018 PhysioNet computing in cardiology challenge [ 48 ], at: (accessed on 24 September 2021).…”

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

“…The correlational dimension D2 can be calculated with both monofractal and multifractal approaches. The Katz fractal dimension (KFD) [82], the Petrosian fractal dimension (PFD) [83], and the Higuchi fractal dimension (HFD) [84] are different approaches to the estimation of the fractal dimension. With multifractal time-series analysis, a fractal spectrum consisting of multiple fractal dimensions can be obtained [85,86].…”

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

“…Therefore, research efforts are underway to determine the minimum number of channels required in both resting state and task-based conditions. Recently, Moctezuma and Molinas (2020) reported on a proof-of-concept study showing that using a dataset of 64 channels from resting state with eyes closed, they were able to obtain a true acceptance rate of up to 0.997 using discrete wavelet transform (DWT) based features with only three channels.…”