An embedded implementation based on adaptive filter bank for brain–computer interface systems

Kais Belwafi, Olivier Romain, Sofien Gannouni, Fakhreddine Ghaffari, Ridha Djemal, Bouraoui Ouni

    Research output: Contribution to journalArticlepeer-review

    54 Scopus citations

    Abstract

    Background: Brain–computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. New-method: This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. Results: The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Comparison-with-existing-method: Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Conclusions: Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates.

    Original languageBritish English
    Pages (from-to)1-16
    Number of pages16
    JournalJournal of Neuroscience Methods
    Volume305
    DOIs
    StatePublished - 15 Jul 2018

    Keywords

    • EEG filter optimization
    • Electroencephalography (EEG)
    • Embedded brain–computer interface (EBCI)
    • Embedded Real-time BCI
    • Motor imagery
    • System on programmable chip (SOPC)
    • Weighted overlap-add (WOLA)

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