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Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study

Raphael Zimmermann12*, Laura Marchal-Crespo34, Janis Edelmann1, Olivier Lambercy1, Marie-Christine Fluet1, Robert Riener34, Martin Wolf2 and Roger Gassert1

Author Affiliations

1 , Rehabilitation Engineering Lab, ETH Zurich, Zurich, Switzerland

2 Biomedical Optics Research Lab, University Hospital Zurich, University of Zurich, Zurich, Switzerland

3 , Sensory-Motor Systems Lab, ETH Zurich, Zurich, Switzerland

4 Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, Zurich, Switzerland

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Journal of NeuroEngineering and Rehabilitation 2013, 10:4  doi:10.1186/1743-0003-10-4

Published: 21 January 2013



Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients.


Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined.


fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062).


This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.

BCI; Single-trial; Hidden Markov model (HMM); Functional NIRS; Biosignals; Autonomic nervous system (ANS); Isometric pinching