An embedded implementation of home devices control system based on brain computer interface

Belwafi Kais, Fakhreddine Ghaffari, Olivier Romain, Ridha Djemal

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

    11 Scopus citations

    Abstract

    This paper presents a new embedded architecture for home devices control system directed through motor imagery actions captured by EEG headset. The proposed system is validated by an offline approach which consists on using available public data-set. These recording are always accompanied with noise and useless information related to the equipment, eyes blinking and many others resources of artifacts. For this reason, a complex EEG signal processing is required; starting by filtering EEG to keep the frequency of interest which is located on μ-rhytm and β-rhytm bands in our case; followed by the extraction of useful feature to minimize the size of EEG data and enhance the probability of classifying each trial correctly. A prototype of our proposed embedded system has been implemented on Stratix IV FPGA Board. The prototype operates at 200 MHz and performs real-time classification with an execution delay of 0.5 second per trial and an accuracy average of 72%.

    Original languageBritish English
    Title of host publication2014 26th International Conference on Microelectronics, ICM 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages140-143
    Number of pages4
    ISBN (Electronic)9781479981533
    DOIs
    StatePublished - 2014
    Event2014 26th International Conference on Microelectronics, ICM 2014 - Doha, Qatar
    Duration: 14 Dec 201417 Dec 2014

    Publication series

    NameProceedings of the International Conference on Microelectronics, ICM
    Volume2015-March

    Conference

    Conference2014 26th International Conference on Microelectronics, ICM 2014
    Country/TerritoryQatar
    CityDoha
    Period14/12/1417/12/14

    Keywords

    • brain computer interface (BCI)
    • EEG filters optimization
    • electroencephalogram (EEG)
    • Motor imagery

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