Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke

Abstract

Background and Purpose-Stroke is a leading cause of long-term motor disability among adults. Present rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment, particularly in relation to hand function. Here we tested the hypothesis that patients experiencing hand plegia as a result of a single, unilateral subcortical, cortical or mixed stroke occurring at least 1 year previously, could be trained to operate a mechanical hand orthosis through a brain-computer interface (BCI). Methods-Eight patients with chronic hand plegia resulting from stroke (residual finger extension function rated on the Medical Research Council scaleϭ0/5) were recruited from the Stroke Neurorehabilitation Clinic, Human Cortical Physiology Section of the National Institute for Neurological Disorders and Stroke (NINDS) (nϭ5) and the Clinic of Neurology of the University of Tübingen (nϭ3). Diagnostic MRIs revealed single, unilateral subcortical, cortical or mixed lesions in all patients. A magnetoencephalography-based BCI system was used for this study. Patients participated in between 13 to 22 training sessions geared to volitionally modulate rhythm amplitude originating in sensorimotor areas of the cortex, which in turn raised or lowered a screen cursor in the direction of a target displayed on the screen through the BCI interface. Performance feedback was provided visually in real-time. Successful trials (in which the cursor made contact with the target) resulted in opening/closing of an orthosis attached to the paralyzed hand. Results-Training resulted in successful BCI control in 6 of 8 patients. This control was associated with increased range and specificity of rhythm modulation as recorded from sensors overlying central ipsilesional (4 patients) or contralesional (2 patients) regions of the array. Clinical scales used to rate hand function showed no significant improvement after training. Conclusions-These results suggest that volitional control of neuromagnetic activity features recorded over central scalp regions can be achieved with BCI training after stroke, and used to control grasping actions through a mechanical hand orthosis. (Stroke. 2008;39:910-917.)

Closed-loop brain training: the science of neurofeedback

“…Features from a set of sensors, such as the power at a frequency window or the level of activation, can be classified as multivariate patterns of activity (MVPAs). The calculated signal is then presented to the individual via visual, auditory 72 , haptic 139 or electrical stimulation 210 feedback, allowing the user to alter neural function and complete the loop with neural processing of feedback. The advantages and disadvantages of these modalities, as well as how their signals are processed for neurofeedback, are discussed in BOX 1.…”

“…Active patient participation in the therapy is another guiding principle of rehabilitation that is derived from studies employing forced use of the affected limb 137 and robotic therapy that minimizes assistance to encourage greater patient effort 138 . One promising avenue for stroke rehabilitation involves exoskeletal training that uses real-time signals from sensorimotor areas 139 .…”

The self-regulating brain and neurofeedback: Experimental science and clinical promise

“…This option receives further credence in findings coming from stroke patients, who manifest impaired muscular control and take much longer-13 to 22 sessions of one-to-two hours-to govern the MEG signal (Buch et al, 2008). Moreover, with one exception (Gallegos-Ayala et al, 2014), patients lacking control of all muscles (e.g., completely locked-in patients) have been entirely unsuccessful in maintaining control over neuroimaging signals (De Massari et al, 2013).…”

Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

“…In a typical trial, the detection of movement intention would trigger a contingent sensory feedback to the user. This feedback can be delivered in an abstract form (e.g., a moving cursor on a computer screen) or as embodied feedback (e.g., visual representations of the participant's body parts over a virtual avatar on a computer screen, in a VR head‐mounted display or directly overlaid on the participant's limbs; or somatosensory representations delivered through robotic, haptic or Neuromuscular Electrical Stimulation (NMES) systems) that reproduces the intended movement, which was shown to enhance motor learning 19, 20, 21…”

“…showed that severely paralyzed chronic stroke patients could learn to control their ipsilesional SMR 21. Since then, an international effort has taken place to investigate whether repeated BCI training can lead to motor recovery.…”