Identification of Real and Imaginary Movements in EEG Using Machine Learning Models

Moreira, J. Agostinho; Moreira, Mário W. L.; Pombo, Nuno; Silva, Bruno M. C.; García, Nuno M.

doi:10.1007/978-3-030-17971-7_70

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…One group of researchers has implemented three classifiers (SVM, LR, and KNN) to distinguish real from imagined movements by using EEG signals [ 195 ]. In this case, the LR algorithm outperformed both SVM and KNN, with overall accuracy rates ranging from 37% to 90%.…”

Section: Discussionmentioning

confidence: 99%

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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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Section: Discussionmentioning

confidence: 99%

“… Own database HHT SVM LDA QDA KNN Accuracy = 89.07 [ 253 ] 2010 MWL 13 subj. 20 channels Own database PCA RBF-SVM - [ 195 ] 2019 MI 11 subj. 3 channels PhysioNet database N/A LR SVM KNN Accuracy = 90 [ 118 ] 2016 SD 22 subj.…”

Section: Table A1mentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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“…In EEG signal processing, the signal information is first extracted and then classified [9]. Among the most popular classification methods are the support vector machine (SVM) [10,11], linear discriminant analysis [12,13], artificial neural networks [14,15], and fuzzy algorithms [16,17]. The accuracy obtained in each classification method also varies in each study, like the results obtained by Farooq et al [18].…”

Section: Introductionmentioning

confidence: 99%

Multilayer extreme learning machine for hand movement prediction based on electroencephalography

2020

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Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.

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