Electrical, Hemodynamic, and Motor Activity in BCI Post-stroke Rehabilitation: Clinical Case Study

Abstract

The goal of the paper 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.

Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review

“…Significant gains in hand function were found for the FMA, ARAT and Jebsen-Taylor Test, after 7-10 weeks of training. This protocol was previously used by their group for training patients post-stroke in multiple studies [86][87][88]. The second study in CP [89] showed a decrease (improvement) in a serial reaction time task with the non-dominant hand, after only three sessions of BCI-NFT (8 min each) using visual feedback.…”

Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness

“…The technology of motor imagery-based on BCI has been used as a treatment for rehabilitation of post stroke and spinal cord injury patients as well as of the individuals with musculoskeletal disorders [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Particular attention is paid to training methods [7,12,18], the use of exoskeletons [9][10][11] and augmented and virtual reality [19,20]. However, the use of BCI in rehabilitation is restricted due to BCI-illiteracy phenomenon: about 10-30% of users cannot control BCI [21].…”

Independent EEG components are meaningful (for BCI based on motor imagery)

“…Brain-computer interface (BCI) is a soft-and hard-ware complex that allows to control external technical devices directly by signals of the brain without commonly used muscle activity. In recent years, motor imagery based BCI systems have become widespread in the motor rehabilitation of patients after a stroke or a trauma [1,10,21,22]. However, as shown [16], BCI technology yields great benefits not only for practical application, but it can also make a major contribution to brain research.…”