High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation. Copyright © 2013 American Neurological Association.

Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain-computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients. Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n = 14) and 1-month MI training without BCI support (control group; n = 14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl-Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings. Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p < 0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p < 0.05). The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments. © 2015 American Neurological Association.

To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77%) and outdoor (69%) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware. Copyright © 2012 Society for Psychophysiological Research.