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

Tiwari, Anurag; Chaturvedi, Amrita

doi:10.1109/access.2021.3110882

Cited by 25 publications

(13 citation statements)

References 67 publications

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“…The ARK-ANN method produced the best results in this cross-subjects comparison with an accuracy of 87.4%, followed by the CNN [32] and DCR [38] methods that are getting almost the same accuracy. Despite achieving a lower level of precision, the DL [33] method is still performing well.…”

Section: Discussionmentioning

confidence: 82%

“…In this extended intra-subjects comparison, the approaches put forth by Xu B et al [32], Tiwari A et al [38] and Zhang R et al [33] performed effectively achieving respectively mean accuracy of 85.6%, 85.4% and 84% of mean accuracy, although the ARK-ANN achieved the best outcomes.…”

Section: Discussionmentioning

confidence: 92%

“…Mean accuracy SNN [31] 81.3% Wavelet T-F Image CNN [32] 85.6% Hybrid DL [33] 84.0% HSS-ELM [34] 79.5% DCR [38] 85.4% ARK-ANN 87.4%…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A novel ANN adaptive Riemannian-based kernel classification for motor imagery

Fodil¹,

Haddab²,

Kachenoura³

et al. 2022

Biomed. Phys. Eng. Express

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More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which use an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86 % for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 92%

See 1 more Smart Citation

A novel ANN adaptive Riemannian-based kernel classification for motor imagery

Fodil¹,

Haddab²,

Kachenoura³

et al. 2022

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…It has not escaped our notice that as SVMs were previously shown to be superior with respect to feature classification (whereas deep learning networks were shown to be superior in BCI feature selection; Li Y. et al, 2019 ; Deng et al, 2021 ; Tiwari and Chaturvedi, 2021 ) a combination of both methods might improve our algorithm further and allow it to generalize to tasks outside of MI or motor control (i.e., non-verbal communication, language decoding, or parsing of thoughts).…”

Section: Discussionmentioning

confidence: 95%

Brain-Computer Interface using neural network and temporal-spectral features

Gan

Cerf

2022

Front. Neuroinform.

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Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.

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“…In Ref. 77 extracted spatial–temporal features using the multivariate empirical mode decomposition were classified with SVM and achieved 85.2%. Also, higher-order dynamic mode decomposition and multichannel singular spectrum decomposition hybridization were considered in Ref.…”

Section: Resultsmentioning

confidence: 99%

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

Salimpour

Kalbkhani

Seyyedi

et al. 2022

Sci Rep

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Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time–frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.

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

Cited by 25 publications

References 67 publications

A novel ANN adaptive Riemannian-based kernel classification for motor imagery

A novel ANN adaptive Riemannian-based kernel classification for motor imagery

Brain-Computer Interface using neural network and temporal-spectral features

Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals

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