EEG pattern decoding of rhythmic individual finger imaginary movements of one hand

Stankevich, Lev; Sonkin, Konstantin; Shemyakina, N. V.; Nagornova, Zh. V.; Khomenko, J. G.; Perets, D. S.; Koval, A. V.

doi:10.1134/s0362119716010175

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…As it was shown before, individual channels selection allowed to increase the decoding accuracy in average by 10 % in 4-class classification [12].…”

Section: Discussionmentioning

confidence: 89%

“…Trials used for decoding didn't have external stimuli in them, but we were able to predict time of imagination because of previously suggested rhythm of imagination. Description of the full paradigm of rhythmically repeated trials with movement imagination for multiclass decoding is performed in [12,13]. In the conditions of practical realization of BCI short time intervals for realization of executive commands are preferable, so as the short time necessary for classification.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Neurological Classifier Committee Based on Artificial Neural Networks and Support Vector Machine for Single-Trial EEG Signal Decoding

Sonkin

Stankevich

Khomenko

et al. 2016

Advances in Neural Networks – ISNN 2016

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Abstract. This study aimed to finding effective approaches for electroencephalographic (EEG) multiclass classification of imaginary movements. The combined classifier of EEG signals based on artificial neural network (ANN) and support vector machine (SVM) algorithms was applied. Effectiveness of the classifier was shown in 4-class imaginary finger movement classification. Nine right-handed subjects participated in the study. The mean decoding accuracy using combined heterogeneous classifier committee was −60 ± 10 %, max: 77 ± 5 %, while application of homogeneous classifier based on committee of ANNs −52 ± 9 % and 65 ± 5 % correspondingly. This work supports the feasibility of the approach, which is presumed suitable for imaginary movements decoding of four fingers of one hand. These results could be used for development of effective non-invasive BCI with enlarged amount of degrees of freedom.

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“…As it was shown before, individual channels selection allowed to increase the decoding accuracy in average by 10 % in 4-class classification [12].…”

Section: Discussionmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Neurological Classifier Committee Based on Artificial Neural Networks and Support Vector Machine for Single-Trial EEG Signal Decoding

Sonkin

Stankevich

Khomenko

et al. 2016

Advances in Neural Networks – ISNN 2016

View full text Add to dashboard Cite

show abstract

“…Similar data processing schemes are also used in other areas, for example, when processing EEG signals [5].…”

Section: Discussionmentioning

confidence: 99%

Development of precursors recognition methods in vector signals

Kapralov

Elagin

Stankevich

et al. 2017

J. Phys.: Conf. Ser.

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“…1). To study the possibility of implementation of a humancomputer interaction system based on a non-invasive BCI, the classifier of imaginary finger movements of one hand, developed by the authors earlier [5,6] was used.…”

Section: Bci Structurementioning

confidence: 99%

Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

2018

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The study describes approaches of direct and supervisor control of a mobile robot based on a non-invasive brain-computer interface. An interface performs electroencephalographic signal decoding, which includes several steps: filtering, artefact detection, feature extraction, and classification. In this study, a classifier with hierarchical structure was developed and applied. Description of a committee of classifiers based on neural networks and support vector machines is given. The developed classifier demonstrated accuracy 50 ± 5% of single trial decoding of four classes of imaginary fine movements. Prospects of using non-invasive brain-computer interface for control of mobile robots was described. Key applications of the system are maintenance of immobilized patients and rehabilitation procedures both in clinic and at home.

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EEG pattern decoding of rhythmic individual finger imaginary movements of one hand

Cited by 18 publications

References 33 publications

Neurological Classifier Committee Based on Artificial Neural Networks and Support Vector Machine for Single-Trial EEG Signal Decoding

Neurological Classifier Committee Based on Artificial Neural Networks and Support Vector Machine for Single-Trial EEG Signal Decoding

Development of precursors recognition methods in vector signals

Mobile robot control based on noninvasive brain-computer interface using hierarchical classifier of imagined motor commands

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