Extracting Patterns of Single-Trial EEG Using an Adaptive Learning Algorithm

Chin-Teng Lin, Yu Kai Wang, Chieh-Ning Fang, Jung Tai King

    Research output: Contribution to conferencePaperpeer-review

    2 Scopus citations

    Abstract

    The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.
    Original languageEnglish
    Pages6642-6645
    Number of pages4
    DOIs
    StatePublished - 2015
    Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
    Duration: 25 Aug 201529 Aug 2015

    Conference

    Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
    Country/TerritoryItaly
    CityMilan
    Period25/08/1529/08/15

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