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Investigating deep learning for fNIRS based BCI.

Johannes Hennrich, Christian Herff, Dominic Heger

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
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    Deep Neural Networks show promise for classifying brain activity using Functional Near infrared Spectroscopy (fNIRS) in Brain Computer Interfaces (BCI). This study explores their effectiveness compared to traditional methods for fNIRS BCI analysis.

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Functional Near infrared Spectroscopy (fNIRS) is an emerging neuroimaging technique for brain activity measurement.
    • Its application in Brain Computer Interfaces (BCI) shows potential but lacks standardized analysis methods.
    • Current fNIRS BCI studies often rely on conventional classifiers and simple features, limiting performance.

    Purpose of the Study:

    • To investigate the feasibility of using Deep Neural Networks (DNNs) for single-trial fNIRS data classification.
    • To compare the performance of DNNs against established methods for fNIRS-based BCI.
    • To explore advanced classification approaches for enhanced fNIRS BCI accuracy.

    Main Methods:

    • Implementation of Deep Neural Networks for analyzing fNIRS brain activation patterns.

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  • Comparison of DNN classification performance with traditional machine learning classifiers.
  • Evaluation of feature extraction and classification strategies for fNIRS signals.

    • Main Results:

    • Deep Neural Networks demonstrate potential for classifying fNIRS brain activation patterns.
    • DNNs offer a more powerful approach compared to conventional methods used in fNIRS BCI.
    • The study provides insights into effective deep learning strategies for fNIRS signal processing.

    Conclusions:

    • Deep Neural Networks represent a viable and powerful tool for advancing fNIRS-based Brain Computer Interfaces.
    • This research opens new avenues for developing more sophisticated and accurate fNIRS BCI systems.
    • Further investigation into DNN architectures and training is recommended for optimal fNIRS BCI performance.