Asynchronous Brain Computer Interfaces Using Echo State Networks

Eyal Zakkay, Amjad Abu-Rmileh, Amir B. Geva, Oren Shriki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Decoding brain activity for brain computer interfaces (BCI) is a challenging machine learning task. Many BCIs are designed in a synchronous setting, which allows to output a command from the brain to the computer at pre-determined, specific time points. The task becomes substantially more demanding when designing asynchronous BCIs, which allow control over the interface at any given time. Most BCIs use static classifiers, in which the readout is driven only by the current input. However, due to the complex nature of ongoing brain activity, dynamic classifiers, which capture the temporal dynamics of the input signal, are likely to be more suitable for the task. To examine this notion, we designed a classification framework based on Echo State Networks (ESNs), which have demonstrated strong performance in similar tasks in other domains. We evaluated the performance of this approach on pre-recorded EEG data of an asynchronous motor imagery (MI) BCI task, and compared our approach to a conventional method. ESN-based architectures exhibited superior performance in comparison. Our findings suggest that ESNs may prove useful in other BCI paradigms in virtue of their ability to reduce the detrimental effect of non-stationarity in brain signals.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
StatePublished - 1 Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow


  • Brain-Computer Interface
  • Echo State Network
  • Electroencephalography
  • Motor-Imagery
  • Reservoir Computing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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