This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. Contents

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.

Silicon photomultipliers are novel solid state photodetectors that recently became commercially available. The goal of this paper was to investigate their suitability for low light level detection in miniaturized functional near-infrared spectroscopy instruments. Two measurement modules with a footprint of 26×26 mm2 were built, and the signal-to-noise ratio was assessed for variable source-detector separations between 25 and 65 mm on phantoms with similar optical properties to those of a human head. These measurements revealed that the signal-to-noise ratio of the raw signal was superior to an empirically derived design requirement for source-detector separations up to 50 mm. An arterial arm occlusion was also performed on one of the authors in vivo, to induce reproducible hemodynamic changes which confirmed the validity of the measured signals. The proposed use of silicon photomultipliers in functional near-infrared spectroscopy bears large potential for future development of precise, yet compact and modular instruments, and affords improvements of the source-detector separation by 67% compared to the commonly used 30 mm.

No abstract

Triggered assistance has been shown to be a successful robotic strategy for provoking motor plasticity, probably because it requires neurologic patients' active participation to initiate a movement involving their impaired limb. Triggered assistance, however, requires sufficient residual motor control to activate the trigger and, thus, is not applicable to individuals with severe neurologic injuries. In these situations, brain and body-computer interfaces have emerged as promising solutions to control robotic devices. In this paper, we investigate the feasibility of a body-machine interface to detect motion execution only monitoring the autonomic nervous system (ANS) response. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) during an isometric pinching task and used to train a classifier based on hidden Markov models. We performed an experiment with six healthy subjects to test the effectiveness of the classifier to detect rest and active pinching periods. The results showed that the movement execution can be accurately classified based only on peripheral autonomic signals, with an accuracy level of 84.5%, sensitivity of 83.8% and specificity of 85.2%. These results are encouraging to perform further research on the use of the ANS response in body-machine interfaces.

This paper presents the concept and initial results of a novel approach for robot assisted sensorimotor training in stroke rehabilitation. It is based on a brain-body-robot interface (B(2)RI), combining both neural and physiological recordings, that detects the intention to perform a motor task. By directly including the injured brain into the therapy, we ultimately aim at providing a new method for severely impaired patients to engage in active movement therapy. In the present study, seven healthy subjects performed an isometric finger pinching task while functional near-infrared spectroscopy (fNIRS) signals from motor cortical areas and biosignals were recorded simultaneously. Results showed an insignificant increase in the blood pressure during the preparation period prior to motor execution. During the execution period, significant changes in oxy-and deoxyhemoglobin were found in the primary motor cortex, accompanied by an increase in blood pressure, respiration rate and galvanic skin response (GSR). Cortical measurements of premotor areas and heart rate revealed significant changes at the subject level with large inter-subject variability. The results presented here will serve as priors for the design of further studies to test the efficacy of the concept with stroke patients, and the found effects will provide a basis for the development of a classifier for a future B(2)RI.

Haptic paddles-low-cost one-degree-of-freedom force feedback devices-have been used with great success at several universities throughout the United States to teach the basic concepts of dynamic systems and physical human-robot interaction (pHRI) to students. The ETHZ haptic paddle was developed for a new pHRI course offered in the undergraduate Mechatronics Focus track of the Mechanical Engineering curriculum at ETH Zurich, Switzerland. Twenty students engaged in this two-hour weekly lecture over the 14 weeks of the autumn 2011 semester, complemented by a weekly two-hour laboratory session with the ETHZ haptic paddle. In pairs, students worked through three common sets of experiments before embarking on a specialization project that investigated one of several advanced topics such as impedance control with force feedback, admittance control, the effect of velocity estimation on stability or electromyographic control. For these projects students received additional hardware, including force sensors, electro-optical encoders or high-performance data acquisition cards. The learning objectives were developed in the context of an accompanying faculty development program at ETH Zurich; a set of interactive sequences and the oral exam were explicitly aligned to these learning objectives. The outcomes of the specialization project presentations and oral exams, and a student evaluation of the course, demonstrated that the ETHZ haptic paddle is a valuable tool that allows students to quite literally grasp abstract principles such as mechanical impedance, passivity and human factors, and helps students create a tangible link between theory and practice in the highly interdisciplinary field of pHRI. Index Terms-Dynamic systems, hands-on laboratory, haptics, human factors, performance metrics, physical human-robot interaction (pHRI), psychophysics, specialization projects.

No abstract