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

Abstract

BackgroundBrain-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.MethodsSeven 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.ResultsfNIRS 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).ConclusionsThis 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.

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

“…Since fNIRI can be miniaturized and made wearable, this offers new fields of application, for instance as a tool to provide novel forms of therapy for severely impaired stroke patients (Zimmermann et al, 2013b), which will lead to a more effective rehabilitation. It can also be applied as method to provide neuro-feedback for basic neuroscientific studies and/or therapeutic interventions (Sulzer et al, 2013;Tinius and Pa, 2005).…”

Evaluating a four-class motor-imagery-based optical brain-computer interface

“…It has also been used alongside bio-signals [10] and to augment existing EEG BCIs [11]. fNIRS uses near infrared light to measure blood oxygenation changes in the brain, which are correlated with functional activity such as cognitive tasks and motor activity [12], [13].…”

Comparison of functional near-infrared spectroscopy and electrodermal activity in assessing objective versus subjective risk during risky financial decisions

“…There are so far few studies that assessed simultaneously the physiological responses of fNIRS and EDA, e.g. (Combe et al, 2010;Kirilina et al, 2012;Matsuo et al, 2003;Solovey et al, 2012;Watanabe et al, 2011;Zimmermann et al, 2013), but not in decision-making research. Moreover, from a methodological point of view, it is entirely unclear whether the two methods have comparable sensitivity in detecting individual risk-related differences.…”