Journal of Neural Engineering · 2007 · 502 citations · 19 references
EngineeringNeuroscienceAssistive TechnologyMotor ControlNeural EngineeringBrain-computer InterfacesElectroencephalographyInfrared SpectroscopyNear-infrared SpectroscopyNeural Recording TechnologiesNeural Signal ProcessingBrain-computer Interface ClassificationBrain-computer Interface Signal AcquisitionBrain-computer Interface Signal ProcessingElectroencephalography-based Brain-computer InterfacesFunctional Near-infrared SpectroscopyMotor Imagery AnalysisNear-infrared Spectroscopy SensorsSignal Acquisition
A brain–computer interface enables thought‑based communication, and near‑infrared spectroscopy offers a safe, portable, and accessible method to record localized cortical activity for such systems. The authors built a simple custom fNIRS‑BCI system, Mindswitch, to evaluate its functionality using motor‑imagery control. The system performs online analysis and provides real‑time feedback, offering a basic on/off switch that takes one minute to select. Initial results demonstrate that fNIRS can support simple BCI tasks, though performance is currently lower than EEG, indicating significant potential for future improvement.
A brain–computer interface (BCI) is a device that allows a user to communicate with external devices through thought processes alone. A novel signal acquisition tool for BCIs is near-infrared spectroscopy (NIRS), an optical technique to measure localized cortical brain activity. The benefits of using this non-invasive modality are safety, portability and accessibility. A number of commercial multi-channel NIRS system are available; however we have developed a straightforward custom-built system to investigate the functionality of a fNIRS-BCI system. This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, 'Mindswitch' that harnesses motor imagery for control. Analysis is performed online and feedback of performance is presented to the user. Mindswitch presents a basic 'on/off' switching option to the user, where selection of either state takes 1 min. Initial results show that fNIRS can support simple BCI functionality and shows much potential. Although performance may be currently inferior to many EEG systems, there is much scope for development particularly with more sophisticated signal processing and classification techniques. We hope that by presenting fNIRS as an accessible and affordable option, a new avenue of exploration will open within the BCI research community and stimulate further research in fNIRS-BCIs.
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