Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2020 | OriginalPaper | Buchkapitel

Transfer Learning Approach in Classification of BCI Motor Imagery Signal

verfasst von : Filip Begiełło, Mikhail Tokovarov, Małgorzata Plechawska-Wójcik

Erschienen in: Computer Information Systems and Industrial Management

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

The paper presents application of a transfer learning-based, deep neural network classification model to the brain-computer interface EEG data. The model was initially trained on the publicly available dataset of motor imagery EEG data gathered from BCI experienced users. The final fitting was performed on the set of six participants for whom it was the first contact with a BCI system. The results show that initial training affects classification accuracy positively even in case of inexperienced participants. In the presented preliminary study five participants were examined. Data from each participant were analysed separately. Results show that the transfer learning approach allows to improve classification accuracy by even more than 10% points in comparison to the baseline deep neural network models, trained without transfer learning.

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
Nächstes Kapitel Time Removed Repeated Trials to Test the Quality of a Human Gait Recognition System
Literatur
1.
Wolpaw, J.R., Wolpaw, E.W.: Brain–computer interfaces: something new under the sun. In: Wolpaw, J.R., Wolpaw, E.W. (eds.) Brain–Computer Interfaces, Principles and Practice, pp. 3–12. Oxford University Press Inc., New York (2012)CrossRef
2.
Clerc, M., Bougrain, L., Lotte, F.: Brain-Computer Interfaces 1: Foundations and Methods. Wiley, New York (2016)CrossRef
3.
Wolpaw, J.R.: Brain–computer interfaces. Handbook of Clinical Neurology 110, 67–74 (2013)CrossRef
4.
Lotte, F., et al.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)CrossRef
5.
Arvaneh, M., Tanaka, T.: Brain–computer interfaces and electroencephalogram: basics and practical issues. In: Arvaneh, M., Tanaka, T. (eds.) Signal Processing and Machine Learning for Brain-Machine Interfaces, pp. 1–21. The Institution of Engineering and Technology, London (2018)MATH
6.
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8, 441–446 (2000)CrossRef
7.
Makeig, S., Kothe, C., Mullen, T., Bigdely-Shamlo, N., Zhang, Z., Kreutz-Delgado, K.: Evolving signal processing for brain–computer interfaces. Proc. IEEE 100, 1567–1584 (2012)CrossRef
8.
Krusienski, D.J., McFarland, D.J., Principe, J.C.: BCI signal processing: Feature extraction. In: Wolpaw, J.R., Wolpaw, E.W. (eds.) Brain–Computer Interfaces, Principles and Practice, pp. 3–12. Oxford University Press Inc., New York (2012)
9.
Cecotti, H.: Feedforward artificial neural networks for event-related potential detection. In: Arvaneh, M., Tanaka, T. (eds.) Signal Processing and Machine Learning for Brain-Machine Interfaces, pp. 173–192. The Institution of Engineering and Technology, London (2018)
10.
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition Neural Comput. 1, 541–551 (1989)
11.
Cecotti, H., Graser, A.: Convolutional neural networks for P300 detection with application to brain–computer interfaces. IEEE Trans. Pattern Anal. Mach. Intell. 33, 433–445 (2011)CrossRef
12.
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)CrossRef
13.
Tabar, Y.R., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14, 016003 (2016)CrossRef
14.
Sturm, I., Lapuschkin, S., Samek, W., Müller, K.R.: Interpretable deep neural networks for single-trial EEG classification. J. Neurosci. Methods 274, 141–145 (2016)CrossRef
15.
Lu, N., Li, T., Ren, X., Miao, H.: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines IEEE. Trans. Neural Syst. Rehabil. Eng. 25, 566–576 (2017)CrossRef
16.
Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58, 355–362 (2011)CrossRef
17.
Kang, H., Nam, Y., Choi, S.: Composite common spatial pattern for subject-to-subject transfer. IEEE Signal Process. Lett. 16, 683–686 (2009)CrossRef
18.
Morioka, H., et al.: Learning a common dictionary for subject-transfer decoding with resting calibration. NeuroImage 111, 167–178 (2015)CrossRef
19.
Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K.R., Grozea, C.: Subject-independent mental state classification in single trials. Neural Netw. 22, 1305–1312 (2009)CrossRef
20.
Cho, H., Ahn, M., Kim, K., Jun, C.S.: Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition. J. Neural Eng. 12, 066009 (2015)CrossRef
21.
Jayaram, V., Alamgir, M., Altun, Y., Scholkopf, B., Grosse-Wentrup, M.: Transfer learning in brain-computer interfaces. IEEE Comput. Intell. Mag. 11, 20–31 (2016)CrossRef
22.
Kang, H., Choi, S.: Bayesian common spatial patterns for multi-subject EEG classification. Neural Netw. 57, 39–50 (2014)CrossRef
23.
Azab, A., Toth, J., Mihaylova, L.S., Arvaneh, M.: A review on transfer learning approaches in brain–computer interface. In: Arvaneh, M., Tanaka, T. (eds.) Signal Processing and Machine Learning for Brain-Machine Interfaces, pp. 173–192. The Institution of Engineering and Technology, London (2018)
24.
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)CrossRef
25.
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)CrossRef
Metadaten
Titel
Transfer Learning Approach in Classification of BCI Motor Imagery Signal
verfasst von
Filip Begiełło
Mikhail Tokovarov
Małgorzata Plechawska-Wójcik
Copyright-Jahr
2020
Buch
Computer Information Systems and Industrial Management

Print ISBN: 978-3-030-47678-6

Electronic ISBN: 978-3-030-47679-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-47679-3_1

Premium Partner

    Bildnachweise