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    Near infrared spectroscopy for brain-computer interface development (Englisch)

    In: International Brain-Computer Interface Workshop and Training Course, 3 ; 104-105  ; 2006

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    A Brain-computer Interface (BCI) can be developed using the optical response of Near Infrared Spectroscopy (NIRS) which measures metabolic brain activation. NIRS can localize brain regions with a spatial resolution in mm and temporal resolution in hundreds of ms. NIRS has the advantages of noninvasiveness, portability and affordability. The authors have developed a multi-channel NIRS-BCI that distinguishes the brain activation during imagination of left hand and right hand movement. This mechanism is being incorporated in a word speller, in an on-going research project, to help severely disabled persons to communicate. Experiments with a volunteer have shown that the HMM (Hidden Markov Model) classifier performers better (average accuracy of 91.29%) than the SVM (Support Vector Machine) classifier (average accuracy of 75.62%). This might be due to considerable variations in the temporal domain in the performance of the tasks, and such variations may be better dealt with by dynamic machines like HMM. NIRS avoids the noise prominent in the EEG, and is less cumbersome to use, as there is no need for applying conducting gel. The most important advantage of the NIRS is its ability to localize brain activity noninvasively. This provides us with an excellent opportunity to use a variety of motor and cognitive tasks, and to detect signals from specific regions of the cortex for the development of powerful and user-friendly BCIs.

    Inhaltsverzeichnis Konferenzband

    Die Inhaltsverzeichnisse werden automatisch erzeugt und basieren auf den im Index des TIB-Portals verfügbaren Einzelnachweisen der enthaltenen Beiträge. Die Anzeige der Inhaltsverzeichnisse kann daher unvollständig oder lückenhaft sein.

    8
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    12
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    14
    Regularised CSP for sensor selection in BCI
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    16
    High frequency bands and estimated local field potentials to improve single-trial classification of electroencephalographic signals
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    22
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    24
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    26
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    28
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    34
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    36
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    40
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    42
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    46
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    54
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    56
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    60
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    64
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    66
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    68
    Brain-computer interface based on non-motor imagery
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    72
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    76
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    78
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    80
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    86
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    92
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    98
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    100
    Heart rate-controlled EEG-based BCI: the Graz hybrid BCI
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    102
    First steps towards the NIRS-based Graz-BCI
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    104
    Near infrared spectroscopy for brain-computer interface development
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    106
    Functional magnetic resonance imaging based BCI for neurorehabilitation
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    108
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    110
    Asynchronous (self-paced) brain-computer communication: exploring the "freeSpace" virtual environment
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    112
    Non-invasive brain-computer interface for mental control of a simulated wheelchair
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    114
    Neural internet for ALS patients
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    116
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    122
    BCI-info.org - An international internet-platform for the BCI community
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    126
    Usage of Simulink for brain-computer interface experiments
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