Genetic-based feature selection for efficient motion imaging of a brain–computer interface framework

Chang, Hongli; Yang, Jimin

doi:10.1088/1741-2552/aad567

Cited by 21 publications

(20 citation statements)

References 53 publications

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“…In reference to [11], authors proposed Hilbert-Huang transform and common spatial subspace decomposition (HCSSD) algorithm to extract time-frequency features and then learning vector quantization neural network (LVQ-NN) was employed to classify the selected features. Chang et al [12] employed stockwell transform (ST) and GA for feature extraction and selection respectively and finally Bayesian LDA (BLDA) was used to classify the selected features. Another study in [13], continuous wavelet transform (CWT) and K-NN are used as features and classifier respectively to classify the ECoG MI data.…”

Section: Introductionmentioning

confidence: 99%

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

et al. 2020

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Brain-computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set.

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Section: Introductionmentioning

confidence: 99%

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

et al. 2020

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“…Brain computer interface (BCI) technology [1][2][3] uses multiple brain function signals, including scalp Electroencephalogram (EEG) [4], Local Field Potentials (LFPs) [5], and Electrocorticography (ECoG) [6], to establish a direct communication channel between human brain and external devices. is characteristic of BCI is extremely important for patients with severe brain nerve damage, since the normal communication channel for such patients has been blocked [7].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network

Miao

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

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EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feature extraction, common spatial pattern (CSP) is one of the most frequently used algorithms. However, in order to extract the optimal CSP features, prior knowledge and complex parameter adjustment are often required. Convolutional neural network (CNN) is one of the most popular deep learning models at present. Within CNN, feature learning and pattern classification are carried out simultaneously during the procedure of iterative updating of network parameters; thus, it can remove the complicated manual feature engineering. In this paper, we propose a novel deep learning methodology which can be used for spatial-frequency feature learning and classification of motor imagery EEG. Specifically, a multilayer CNN model is designed according to the spatial-frequency characteristics of MI EEG signals. An experimental study is carried out on two MI EEG datasets (BCI competition III dataset IVa and a self-collected right index finger MI dataset) to validate the effectiveness of our algorithm in comparison with several closely related competing methods. Superior classification performance indicates that our proposed method is a promising pattern recognition algorithm for MI-based BCI system.

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“…The classification accuracy of the proposed framework is evidently higher than that of the previously used algorithms. Chang et al [ 6 ] proposed a feature selection scheme based on a genetic algorithm; the classification accuracy of the algorithm is 96%, and the number of selected features is 48.6% relative to the number of original features. By contrast, our scheme achieves 99% accuracy with less than 10.5% features, which proves the effectiveness of the scheme proposed in the present study.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, task-related features require proper selection mechanisms. Although a genetic algorithm is used most commonly for task recognition [ 5 , 6 ], different MI optimization methods have been suggested, such as differential evolution [ 7 ], particle swarm optimization [ 8 ], concave-convex procedure [ 9 ], principal component analysis [ 10 ], and correlation-based channel and time window selection [ 11 , 12 ]. It should be noted that the PSO algorithm is another promising technique with simple computation and rapid convergence characteristics, which has been successfully applied to mechanical engineering optimization, business optimization, and clustering problems [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization

Ding

et al. 2020

Computational Intelligence and Neuroscience

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Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. The BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme. Compared to a method without optimization (89% accuracy), the best classification accuracy of the PSO-based scheme was 99% when less than 10.5% of the original features were used, the test time was reduced by more than 90%, and it achieved Kappa values and F-score of 0.98 and 98.99%, respectively, and better signal-to-noise ratio, thereby outperforming existing algorithms. The results show that the channel and feature selection scheme can accelerate the speed of convergence to the global optimum and reduce the training time. As the proposed framework can significantly improve classification performance, effectively reduce the number of features, and greatly shorten the test time, it can serve as a reference for related real-time BCI application system research.

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Genetic-based feature selection for efficient motion imaging of a brain–computer interface framework

Abstract: As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.

Cited by 21 publications

References 53 publications

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network

Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization

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