Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke

Abstract

Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.

Keywords: action observation; biomarkers; brain-computer interface; chronic stroke; electroencephalography; motor rehabilitation.

Figure 1

(A) Experimental setup of the BCI training and the analysis of offline data in biomarker analysis. (B) The timeline of recording resting state EEG while observation of non-biological movements. (C) The timing for BCI training while observation of biological movements.

Figure 2

Correlation analysis between averaged contralateral EEG power and clinical scales pre-training and post-training. The values of the correlation coefficient (c.c) are presented in the color of circles. The value of ps are presented with the number in circles and related to the size of the circles (larger size corresponds to the lower value of p).

Figure 3

Average ipsilesional EEG power variation (relative to the first session) during 20 sessions of training. Rows show EEG in different bands. Columns show EEG in different states. Red lines and dots: patients with good recovery (reaching the MCID level). Black lines and dots: patients with poor recovery (not reaching the MCID level). Bold and dark lines: averaged data from patients with and without effective recovery. * indicates the significant (p < 0.01) result in corresponding training session. ** represent significant results in two consecutive sessions.

Figure 4

Correlation analysis of averaged (A) delta, (B) theta, (C) alpha, (D) low-beta, and (E) high-beta power variation in the affected hemisphere during rest and clinical scale variation after 20 sessions of training. Each point denotes data from one subject. Correlation analyses: two-tailed Spearman’s correlation coefficient.

Figure 5

Correlation analysis of averaged (A) delta, (B) theta, (C) alpha, (D) low-beta, and (E) high-beta power variation in the affected hemisphere during task and clinical scale variation after 20 sessions of training. Correlation analyses: two-tailed Spearman’s correlation coefficient (Significance: *p < 0.01).

Figure 6

Correlation analysis of averaged (A) delta, (B) theta, (C) alpha, (D) low-beta, and (E) high-beta task/rest power ratio variation in the affected hemisphere and clinical scale variation after 20 sessions of training. Correlation analyses: two-tailed Spearman’s correlation coefficient. (Significance: *p < 0.01, **p < 0.001).

Figure 7

Significance of correlation between clinical improvements and variation of EEG power during rest and task in different electrodes.