Marie-Christine Fluet

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

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.

Upper limb assessment using a Virtual Peg Insertion Test

This paper presents the initial evaluation of a Virtual Peg Insertion Test developed to assess sensorimotor functions of arm and hand using an instrumented tool, virtual reality and haptic feedback. Nine performance parameters derived from kinematic and kinetic data were selected and compared between two groups of healthy subjects performing the task with the dominant and non-dominant hand, as well as with a group of chronic stroke subjects suffering from different levels of upper limb impairment. Results showed significantly smaller grasping forces applied by the stroke subjects compared to the healthy subjects. The grasping force profiles suggest a poor coordination between position and grasping for the stroke subjects, and the collision forces with the virtual board were found to be indicative of sensory deficits. These preliminary results suggest that the analyzed parameters could be valid indicators of impairment.

Assessment of upper limb motor function in patients with multiple sclerosis using the Virtual Peg Insertion Test: A pilot study

Quantifying and tracking upper limb impairment is of key importance to the understanding of disease progress, establishing patient-tailored therapy protocols and for optimal care provision. This paper presents the results of a pilot study on the assessment of upper limb motor function in patients with multiple sclerosis (MS) with the Virtual Peg Insertion Test (VPIT). The test consists in a goal-directed reaching task using a commercial haptic display combined with an instrumented handle and virtual environment, and allows for the extraction of objective kinematic and dynamic parameters. Ten MS patients and eight age-matched healthy subjects performed five repetitions of the VPIT with their dominant and non-dominant hand. Upper limb movements were found to be significantly slower, less smooth and less straight compared to healthy controls, and the time to complete the VPIT was well correlated with the conventional Nine Hole Peg Test (r=0.658, p<;0.01). Tremor in the range of 3-5 Hz could be detected and quantified using a frequency analysis in patients featuring intention tremor. These preliminary results illustrate the feasibility of using the VPIT with MS patients, and underline the potential of this test to evaluate upper limb motor function and discriminate characteristic MS related impairments.

Towards a BCI for sensorimotor training: Initial results from simultaneous fNIRS and biosignal recordings

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 (B2RI), 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 B2RI.

The Virtual Peg Insertion Test as an assessment of upper limb coordination in ARSACS patients: A pilot study

OBJECTIVE This paper introduces a novel assessment tool to provide clinicians with quantitative and more objective measures of upper limb coordination in patients suffering from Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS). The Virtual Peg Insertion Test (VPIT) involves manipulating an instrumented handle in order to move nine pegs into nine holes displayed in a virtual environment. The main outcome measures were the number of zero-crossings of the hand acceleration vector, as a measure of movement coordination and the total time required to complete the insertion of the nine pegs, as a measure of overall upper limb performance. RESULTS 8\9 patients with ARSACS were able to complete five repetitions with the VPIT. Patients were found to be significantly less coordinated and slower than age-matched healthy subjects (p<0.01). Performance of ARSACS patients was positively correlated with the Nine-Hole Peg Test (r=0.85, p<0.01) and with age (r=0.93, p<0.01), indicative of the degenerative nature of the disease. CONCLUSION(S) This study presents preliminary results on the use of a robotics and virtual reality assessment tool with ARSACS patients. Results highlight its potential to assess impaired coordination and monitor its progression over time.

Effects of 2D/3D visual feedback and visuomotor collocation on motor performance in a Virtual Peg Insertion Test

This paper evaluates the influence of three different types of visual feedback on the motor performance of healthy subjects during the repeated execution of a Virtual Peg Insertion Test developed for the assessment of sensorimotor function of arm and hand in neurologically impaired subjects. One test trial consists of the grasping and insertion of 9 pegs into 9 holes using a haptic display with instrumented grasping handle. Three groups performed 10 trials initially on three different setups (group 1 with standard 2D visual feedback, group 2 with 3D, and group 3 with collocated 3D visual feedback) followed by 10 more trials with the setup with 2D visual feedback. The total execution time and the mean collision force as well as the time and the collision force for 6 different movement phases were compared between groups and analyzed in function of the number of repetitions. Results showed significantly lower time to approach and align the visual cursor with the peg with the 2D setup over the first 10 trials compared to the two other groups, suggesting limitations of the 3D setup. Furthermore, a significant decrease of the total execution time was found in the first 10 trials for all groups. For the 10 following trials, only group 3 showed a significant decrease in the total execution time, suggesting that the learning did not transfer to the 2D setup for this group.

What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation

BCIs have recently been identified as a method to promote restorative neuroplastic changes in patients with severe motor impairment, such as after a stroke. In this chapter, we describe a novel therapeutic strategy for hand rehabilitation making use of this method. The approach consists of recording brain activity in cortical motor areas by means of near-infrared spectroscopy, and complementing the cortical signals with physiological data acquired simultaneously. By combining these signals, we aim at detecting the intention to move using a multi-modal classification algorithm. The classifier output then triggers assistance from a robotic device, in order to execute the movement and provide sensory stimulation at the level of the hand as response to the detected motor intention. Furthermore, the cortical data can be used to control audiovisual feedback, which provides a context and a motivating training environment. It is expected that closing the sensorimotor loop with such a brain-body-robot interface will promote neuroplasticity in sensorimotor networks and support the recovery process.