Nothing useful here, move along.
- 1Research Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
- 2Laboratory of Mathematical Neurobiology of
Learning of Institute of Higher Nervous Activity and Neurophysiology,
Russian Academy of Science, Moscow, Russia
- 3Faculty of Physics, Moscow State University, Moscow, Russia
- 4Department of Neurology, Vladimirsky Moscow Regional Research Clinical Institute, Moscow, Russia
- 5CNRS, Marseille, France
The goal of the paper
(the goal should be to create a protocol for this, not just lazily describe something. Useless as is. ) is to present an example of integrated analysis
of electrical, hemodynamic, and motor activity accompanying the motor
function recovery in a post-stroke patient having an extensive cortical
lesion. The patient underwent a course of neurorehabilitation assisted
with the hand exoskeleton controlled by brain-computer interface based
on kinesthetic motor imagery. The BCI classifier was based on
discriminating covariance matrices of EEG corresponding to motor
imagery. The clinical data from three successive 2 weeks
hospitalizations with 4 and 8 month intervals, respectively were under
analysis. The rehabilitation outcome was measured by Fugl-Meyer scale
and biomechanical analysis. Both measures indicate prominent improvement
of the motor function of the paretic arm after each hospitalization.
The analysis of brain activity resulted in three main findings. First,
the sources of EEG activity in the intact brain areas, most specific to
motor imagery, were similar to the patterns we observed earlier in both
healthy subjects and post-stroke patients with mild subcortical lesions.
Second, two sources of task-specific activity were localized in primary
somatosensory areas near the lesion edge. The sources exhibit
independent mu-rhythm activity with the peak frequency significantly
lower than that of mu-rhythm in healthy subjects. The peculiarities of
the detected source activity underlie changes in EEG covariance matrices
during motor imagery, thus serving as the BCI biomarkers. Third, the
fMRI data processing showed significant reduction in size of areas
activated during the paretic hand movement imagery and increase for
those activated during the intact hand movement imagery, shifting the
activations to the same level. This might be regarded as the general
index of the motor recovery. We conclude that the integrated analysis of
EEG, fMRI, and motor activity allows to account for the reorganization
of different levels of the motor system and to provide a comprehensive
basis for adequate assessment of the BCI+ exoskeleton rehabilitation
efficiency.
More details at link if you want to wade through them. I still got nothing out of it.
Alexander A. Frolov1,2*, 
Pavel D. Bobrov1,2*, 
Elena V. Biryukova1,2*, 
Anna V. Silchenko2,3, 
Anna A. Kondur4, 
Indiko Z. Dzhalagoniya2 and 
Jean Massion5
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