EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization

Moctezuma, Luis Alfredo; Molinas, Marta

doi:10.3389/fnins.2020.00593

Cited by 63 publications

(43 citation statements)

References 56 publications

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“…In this problem, a bi-objective function with two criteria (error rate and the number of channels) was used to obtain a robust trade-off between the number of channels and the classification accuracy. In recent work, the Multi-Objective Non-Sorting Genetic Algorithm (MO-NSGA) algorithm, is used for channel selection [48]. This method employed a hybrid signal feature set using Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) with MO-NSGA algorithm and achieved 100% classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Tiwari

Chaturvedi

2021

IEEE Access

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Brain-Computer Interface (BCI) provides a direct communicating pathway between the human brain and the external environment. In the BCI systems, electroencephalography (EEG) signals are used to represent different cognitive patterns corresponding to various limb movements or motor imagery (MI) activities. However, EEG signals are multichannel in nature that require explicit information processing to alleviate the computational complexity of the BCI system. This paper represents a novel channel selection method to find effective EEG channels using Dynamic Channel Relevance (DCR) score. The proposed approach explores the minimum redundancy maximum relevance paradigm for selecting pertinent channels from raw ones. Firstly, the EEG signals are preprocessed using the Savitzky-Golay filter, and a sliding window approach is used to decompose them into chunks of fixed length. Further, three priorly known channels are used as candidate solutions for detecting new correlated channels. The selected channels are used to extract spatial-temporal features using the Multivariate Empirical Mode Decomposition (MEMD) method. The extracted features are used to discriminate four MI tasks (left hand, right hand, tongue, and foot) using the Support Vector Machine (SVM). The experiment is validated on three public EEG datasets (BCI Competition IV-2008 -IIA, BCI Competition IV-dataset 1, BCI competition III -dataset IVa). The results show that the proposed method achieved a superior classification accuracy (85.4% on dataset 1, 80.33% on dataset 2, and 85.20% on dataset 3) with a lesser number of channels compared to state-of-the-art methods. In addition, our method significantly reduced the computation time compared to other published results without compromising the classification accuracy. Topographical mapping between the selected channels and the cognitive regions showed that the central, frontal, and parietal lobe contributes to the execution of various MI tasks during physical activities.

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Section: Related Workmentioning

confidence: 99%

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Tiwari

Chaturvedi

2021

IEEE Access

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“…This algorithm has been applied to EEG channel selection for classification of motor imagery Kee et al (2015). Moreover, the algorithm has proven to be effective in identifying low-density EEG subsets that maximize classification accuracy while reducing the number of EEG channels required for epileptic seizure classification (Moctezuma and Molinas (2020a)) and subject identification (Moctezuma and Molinas (2020b)).…”

Section: Electrode Optimization For Source Reconstructionmentioning

confidence: 99%

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

Soler

Moctezuma

Giraldo

et al. 2021

Preprint

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High-density Electroencephalography (HD-EEG) has been proven to be the most accurate option to estimate the neural activity inside the brain. Although multiple studies report the effect of electrode number on source localization for specific sources and specific electrode configurations, the electrodes for each configuration have been manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, where electrodes were not selected according to their contribution to accuracy. In this work, an optimization-based study aimed to determine the minimum number of electrodes and identify optimal combinations of electrodes that can keep the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single and multiple source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that minimize (1) the localization error for each source and (2) the number of required EEG electrodes. It can be used for evaluating the source localization quality of low-density EEG systems (consumer-grade wearable EEG) We performed an evaluation over synthetic and real EEG dataset with known ground-truth. The experimental results shown that selected electrode combinations with 6 electrodes can obtain for a single source case, an equal or better accuracy than HD-EEG (with more than 200 channels) when reconstructing a particular brain activity in more than 88% of the cases (in synthetic signals) and 63% (in real signals), and more than 88% and 73% of the cases when considering combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that an equal or better accuracy than HD-EEG with 231 electrodes was attained in at least 58%, 76%, and 82% of the cases, when using optimized combinations of 8, 12, and 16 electrodes, respectively. Additionally, in such electrode numbers a lower mean error and standard deviation than with 231 electrodes was obtained.

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“…Nevertheless, none of those studies showed the machine learning validating performance comparisons between results with selected channels and results with all channels. Moctezuma and Molinas [ 15 ] decomposed the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT) for the channel selection. The results showed accuracies of up to 100% with only one EEG channel in the epileptic seizure classification, while all the test results of channels were less than 100%; however, this research only classified the seizure and non-seizure signals, not the pre-ictal signals.…”

Section: Introductionmentioning

confidence: 99%

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

2021

Sensors

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The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

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EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization

Cited by 63 publications

References 56 publications

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

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