Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification

Miao, Minmin; Wang, Aimin; Liu, Feixiang

doi:10.1007/s00521-017-2950-7

Cited by 23 publications

(21 citation statements)

References 37 publications

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“…For a classification task, Kevric and Subasi [14] e system performance is tested by using the publicly available BCI competition III dataset IVa. Miao et al [15] have suggested an EEG signals channel selection method. It uses the linear discriminant criteria for automated selection of channels with strong discriminative capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

Subaşı

Qaisar

2021

Journal of Healthcare Engineering

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The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.

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Section: Literature Reviewmentioning

confidence: 99%

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

Subaşı

Qaisar

2021

Journal of Healthcare Engineering

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“…The BCI is the key for the human brain to communicate with the outside world. It is a non-muscle channel communication method [ 2 , 3 , 4 ]. Currently, there are two brain-computer interface technologies, invasive and non-invasive.…”

Section: Introductionmentioning

confidence: 99%

“…Through EEG signals, patients’ brain activity can be analyzed, helping patients communicate with the outside world, improving their quality of daily life, and reducing their mental burden [ 11 ]. Motor imagery can activate the sensorimotor cortex, produce sensorimotor concussion, send out EEG signals, and reflect the subject’s motor intention [ 2 , 12 ]. When the subject imagines a certain part of the limb movement rather than the actual movement, the corresponding reflex area in the human brain will display electrical potential changes.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have found that when a subject imagines the movement of a certain limb, the neuron activity will appear in the brain area related to this movement. This energy change is called event-related desynchronization (ERD) and event-related synchronization (ERS) [ 2 , 12 , 13 ]. The most successful method for detecting ERD/ERS is common spatial patterns (CSP).…”

Section: Introductionmentioning

confidence: 99%

“…However, the information of the motor imagery of EEG signals is reflected not only in the frequency and spatial characteristics, but also in the features of the time domain and time-frequency domain [ 22 ]. Therefore, only the frequency domain and the spatial domain are considered, and other domain characteristics are ignored, that may result in the loss of information related to motor imagery, and the lost information may be very helpful for the recognition of motor imagination tasks [ 2 ]. In recent years, deep learning has become a research hotspot in various fields, and the use of deep learning to decode EEG signals has attracted the attention of many scholars.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Zheng

Wang

et al. 2020

Sensors

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Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

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A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

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Multi-class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi-channel EEG activity still remains a challenging task. In this study, to enhance the multi-class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed.First, multi-channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA-SOGWO based EEG channel selection criterion.After that, the discriminable feature-set is generated from time, frequency, and FAWT based time-frequency domain of EEG dataset. Then, an informative feature-set is constructed from extracted features through CCA-RFE based feature selection criterion. Finally, training and validation of selected feature-set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi-class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

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Application of artificial bee colony algorithm in feature optimization for motor imagery EEG classification

Cited by 23 publications

References 37 publications

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

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