We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values. The obtained feature vectors are then iteratively tested for solving two unconstrained objectives by NSGA-II or NSGA-III; to maximize classification accuracy and to reduce the number of EEG channels required for epileptic seizure classification. Our results have shown accuracies of up to 1.00 with only one EEG channel. Interestingly, when using all the EEG channels available, lower accuracies were achieved compared to the case when EEG channels were selected by NSGA-II or NSGA-III; i.e., in patient 19 we obtained an accuracy of 0.95 using all the channels and 0.975 using only two channels selected by NSGA-III. The results obtained are encouraging and it has been shown that it is possible to classify epileptic seizures using a few electrodes, which provide evidence for the future development of portable EEG seizure detection devices.
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.
A new concept of low-density electroencephalograms-based (EEG) Subject identification is proposed in this paper. To that aim, EEG recordings of resting-states were analyzed with 3 different classifiers (SVM, k-NN, and naive Bayes) using Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) for feature extraction and their accuracies were estimated to compare their performances. To explore the feasibility of using fewer channels with minimum loss of accuracy, the methods were applied to a dataset of 27 Subjects (From 5 sessions of 30 instances per Subject) recorded using the EMOTIV EPOC device with 1 set of 14 channels and 4 subsets (8, 4, 2 and 1 channel) that were selected using a greedy algorithm. The experiments were reproduced using fewer instances each time to observe the evolution of the accuracy using both; fewer channels and fewer instances. The results of this experiments suggest that EMD compared with DWT is a more robust technique for feature extraction from brain signals to identify Subjects during resting-states, particularly when the amount of information is reduced: e.g., using Linear SVM and 30 instances per Subject, the accuracies obtained using 14 channels were 0.91 and 0.95, with 8 channels were 0.87 and 0.89 with EMD and DWT respectively but were reversed in favor of EMD when the number of channels was reduced to 4 channels (0.76 and 0.74), 2 (0.64 and 0.56) and 1 channel (0.46 and 0.31). The general observed trend is that, Linear SVM exhibits higher accuracy rates using high-density EEG (0.91 with 14 channels) while Gaussian naive Bayes exhibits better accuracies when using low-density EEG in comparison with the other classifiers (With EMD 0.88, 0.81, 0.76 and 0.61 respectively for 8, 4, 2 and 1 channel). The findings of these experiments reveal an important insight for continuing the exploration of low-density EEG for Subject identification.
When brain activity is translated into commands for real applications, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a Biometric Recognition System. For each EEG channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on the Minkowski distance, and for each IMF 4 features are computed: Instantaneous and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To test the proposed method, a dataset with 20 subjects who imagined 30 repetitions of 5 words in Spanish, is used. Four classifiers are used for this task -random forest, SVM, naive Bayes and k-NN -and their performances are compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds cross-validation suggest that the proposed method based on EMD can be valuable for creating EEG-based biometrics of imagined speech for Subjects identification.
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