nach oben

Optical Memory and Neural Networks

Erschienen in:

01.04.2019

Motor Imagery-based Brain-Computer Interface: Neural Network Approach

verfasst von: D. M. Lazurenko, V. N. Kiroy, I. E. Shepelev, L. N. Podladchikova

Erschienen in: Optical Memory and Neural Networks | Ausgabe 2/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A neural network approach has been developed for detecting EEG patterns accompanying the implementation of motor imagery, which are mental equivalents of real movements. The method is based on Local Approximation of Spectral Power using Radial Basis Functions (LASP-RBF) and the original algorithm for interpreting the time sequence of neural network responses. An asynchronous neural interface has been created, the basic element of which is a committee of three neural networks providing the classification of target EEG patterns accompanying the execution of motor imagery by the upper and lower limbs. A comparative evaluation of the classification efficiency of EEG patterns of mental equivalents of real movements was carried out using the developed classifier and traditional classification methods in particular, Random Forest, Linear Discriminant Analysis and Linear Regression methods. It was shown that the classification accuracy using the developed approach is higher (up to 90%) than other classifiers.

Vorheriger Artikel Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks

Nächster Artikel A Hybrid Learning Approach for Adaptive Classification of Acoustic Signals Using the Simulated Responses of Auditory Nerve Fibers

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Kato, K., Takahashi, K., Mizuguchi, N., and Ushiba, J., Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm, J. Neurosci. Methods, 2018, vol. 293, pp. 289–298.CrossRef

Scherer, R., and Vidaurre, C., Motor imagery-based brain–computer interfaces, in Smart Wheelchairs and Brain-Computer Interfaces, Academic Press, 2018, pp. 171–195.

Gao, Q., Zhao, X., Yu, X., Song, Y., and Wang, Z., Controlling of smart home system based on brain-computer interface, Technol. Health Care, 2018, pp. 1–15.

Novak, D., Sigrist, R., Gerig, N.J., Wyss, D., Bauer, R., Götz, U., and Riener, R., Benchmarking brain-computer interfaces outside the laboratory: The Cybathlon 2016, Front. Neurosci., 2018, vol. 11, p. 756.CrossRef

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., and Yger, F., A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update, J. Neural Eng., 2018, vol. 15, no. 3, p. 031005.CrossRef

Yeo, S.N., Lee, T.S., Sng, W.T., Heo, M.Q., Bautista, D., Cheung, Y.B., and Zhou, J., Effectiveness of a personalized brain-computer interface system for cognitive training in healthy elderly: A randomized controlled trial, J. Alzheimer’s Dis., 2018, pp. 1–12.

Hettiarachchi, I.T., Babaei, T., Nguyen, T., Lim, C.P., and Nahavandi, S., A fresh look at functional link neural network for motor imagery-based brain–computer interface, J. Neurosci. Methods, 2018, vol. 305, pp. 28–35.CrossRef

Sánchez-Reolid, R., García, A., Vicente-Querol, M., Fernández-Aguilar, L., López, M., and González, A., Artificial neural networks to assess emotional states from brain-computer interface, Electronics, 2018, vol. 7, no. 12, p. 384.CrossRef

Chakladar, D.D., and Chakraborty, S., Feature extraction and classification in brain-computer interfacing: Future research issues and challenges, Natural Computing for Unsupervised Learning, Cham: Springer, 2019, pp. 101–131.

10.

Meisheri, H., Ramrao, N., and Mitra, S., Multiclass common spatial pattern for EEG based brain computer interface with adaptive learning classifier, 2018. arXiv preprint arXiv:1802.09046.

11.

Shepelev, I.E., Lazurenko, D.M., Kiroy, V.N., Aslanyan, E.V., Bakhtin, O.M., and Minyaeva, N.R., A novel neural network approach to creating a brain–computer interface based on the EEG patterns of voluntary muscle movements, Neurosci. Behav. Physiol., 2018, vol. 48, no. 9, pp. 1145–1157.CrossRef

12.

Chidi, A., Hanafusa, Y., Itakura, T., and Tanaka, T., Simultaneous observation and imagery of hand movement enhance event-related desynchronization of stroke patients, in Advances in Cognitive Neurodynamics (VI), Singapore: Springer, 2018, pp. 71–77.

13.

Gruenwald, J., Kapeller, C., Guger, C., Ogawa, H., Kamada, K., and Scharinger, J., Comparison of Alpha/Beta and high-gamma band for motor-imagery based BCI control: A qualitative study, Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference, 2017, pp. 2308–2311.

14.

Jahangiri, A. and Sepulveda, F., The relative contribution of high-gamma linguistic processing stages of word production, and motor imagery of articulation in class separability of covert speech tasks in EEG data, J. Med. Syst., 2019, vol. 43, no. 2.

15.

McFarland, D.J., and Wolpaw, J.R., Brain–computer interface use is a skill that user and system acquire together, PLoS Biol., 2018, vol. 16, no. 7, e2006719.CrossRef

16.

Birbaumer, N. and Hochberg, L.R., A useful communication in brain-computer interfaces, Neurology, 2018, vol. 91, no. 3, pp. 109–110.CrossRef

17.

Wolpaw, J.R., Bedlack, R.S., Reda, D.J., Ringer, R.J., Banks, P.G., Vaughan, T.M., and McFarland, D.J., Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis, Neurology, 2018, vol. 91, no. 3, pp. 258–267.CrossRef

18.

Kaplan, A., Vasilyev, A., Liburkina, S., and Yakovlev, L., Poor BCI performers still could benefit from motor imagery training, International Conference on Augmented Cognition, Cham: Springer, 2016, pp. 46–56.

19.

Mzurikwao, D., Ang, C.S., Samuel, O.W., Asogbon, M.G., Li, X., and Li, G., Efficient channel selection approach for motor imaginary classification based on convolutional neural network, 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2018, pp. 418–421.

20.

Braga, R.B., Lopes, C.D., and Becker, T., Round cosine transform based feature extraction of motor imagery EEG signals, World Congress on Medical Physics and Biomedical Engineering 2018, Singapore: Springer, 2019, pp. 511–515.

21.

Tayeb, Z., Fedjaev, J., Ghaboosi, N., Richter, C., Everding, L., Qu, X., and Conradt, J., Validating deep neural networks for online decoding of motor imagery movements from EEG signals, Sensors, 2019, vol. 19no. 1, p. 210.

22.

Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., and Cichocki, A., Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces, Expert Syst. Appl., 2018, vol. 96, pp. 302–310.CrossRef

Titel: Motor Imagery-based Brain-Computer Interface: Neural Network Approach
verfasst von: D. M. Lazurenko
V. N. Kiroy
I. E. Shepelev
L. N. Podladchikova
Publikationsdatum: 01.04.2019
Verlag: Pleiades Publishing
Erschienen in: Optical Memory and Neural Networks / Ausgabe 2/2019
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X19020097

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2019

Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks

Parametric Space Dimensionality Reduction in Multidimensional Signal Interpolation

Effect of Alternate Polarization for SD-WDM System Using Hybrid Optical Amplifier

Performance Analysis of Hybrid 2D Codes at Constant Weight Using (n, w, λa, λc) OOCs for OCDMA

Next Neighbors Addition-Induced Change of 2D Ising Model Critical Parameters

Study of Fault Tolerance Methods for Hardware Implementations of Convolutional Neural Networks