Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study

Abstract

Background: In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns' changes after a short-term rehabilitation training.

Materials and methods: Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal's Mu band power's attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG's Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain.

Results: Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group's ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group's Mu band power's attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = -0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group's network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group's most network parameters didn't change significantly (t-test value: p > 0.05).

Conclusion: The MI-BCI training's short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.

Keywords: brain-computer interface; brain-network analysis; electroencephalogram; event-related potentials; motor imagery; neurofeedback-rehabilitation; short-term training.

FIGURE 1

Experimental Paradigm. (A) The experimental paradigm of the data is divided into three 15-min sessions, each containing 300 BCI trials, with an average duration of 3 s per trial, including about 2 s of pause and 1 s of the action. (B) Icons seen by the subjects during the non-feedback experiment. Subjects follow the instructions in the red box for the MI task. (C) Computer instructions for Feedback experiment. Subject are able to move the robotic-arms as feedback in the MI-task.

FIGURE 2

The MuSC of subject A and B. (A) the MuSC for non-feedback subject A (3-day experimental data are synchronized and averaged according to a set of 20 trials). The red line is a linear fit, where the slope of line A is negative (slope = −0.4). (B) the MuSC for Subject B, the slope of line B is positive (slope = 0.2).

FIGURE 3

ERP and topographic comparisons between the 1st and 3rd super-trials of the short-term BCI training process. This comparison was for feedback subject B. Each super-trials containing consecutive 100 non-hold trials. (A) Filter with 0.3–30 Hz. No significant change between the 1st and 3rd super-trials. Some drift changes were present in the prefrontal channels. (B) Filter with 3–30 Hz. The 1st and 3rd topographic maps show dynamic differences. N-potential attenuation at 0.35 s, P-potential enhanced at 0.55 s, then N-potential enhanced at 0.65 s.

FIGURE 4

The overall ERP performance of the feedback and non-feedback subject. (A) Non-feedback subject A, the potential graph of each channel during left- and right- handed MI training (−0.5∼1 s). (B) Feedback subject B, the potential graph of each channel during left- and right- handed MI training (−0.5∼1 s). Both subjects present clear ERP curves, and the ERP curves of the left channels and the right channels show slight differences at different MI task.

FIGURE 5

(A) A schematic representation of the one-node degree analysis. (B) Single node degree after averaging the three non-feedback trials of subject A, the effect tends to be smooth, where the contralateral Fp node degree shows a significant change of 1–2 super-trial (t-test value p < 0.05). (C) The single node degree of subject B, both the ipsilateral and contralateral single nodes have a decrease relative to the initial value (t-test value C’s ipsilateral:s = 5.60, p < 0.01; s = 2.97, p < 0.01, C’s contralateral: s = 10.40, p < 0.01, s = 3.13, p < 0.01, Fp’s ipsilateral: s = 5.69, p < 0.01, s = −7.09, p < 0.01, Fp’s contralateral: s = 6.85, p < 0.01, s = −8.08, p < 0.01). The symbols * and ** represent the mark of significant and very significant changed data.

FIGURE 6

(A) A schematic representation of the nodes included in the three computational methods, from top to bottom, Ex, LnL, and LnR. (B) Scatter plot of the brain network indicators in the MI task state of Subject A and calculates the linear regression fitted straight lines for the three scatter types. Among them, B-figure left EX,LNL,LNR; (C) Scatter plots of network indicators in subject B’s feedback experimental data, and the slopes of all straight lines fitted are negative, (B,C) indicate the gradients of LnR in their leftMI are all less than LnL.

FIGURE 7

(A) Clustering coefficients histograms of non-feedback subject A, left, middle and right plots were calculated for left-handed MI, right-handed MI, and rest condition, a significant decrease in the right hemispheric region value in 1–2 trials during left-handed MI (t-test value p < 0.05), rest condition The all-brain indicator was also significantly different (t-test value p < 0.05); (B) clustering coefficients of feedback subject B, there was an extremely significant downward trend in the left-handed MI for both the all-brain and right hemisphere indicators 1–2, 1–3 (t-test value p < 0.01), left hemisphere had an extremely significant difference only between 1 and 3 experimental comparisons (t-test value p < 0.05). In rightMI, all-brain had a significant decrease between 1 and 2, 1 and 3 super-trials (p < 0.01). Left hemisphere and right hemisphere indicators have significant changed between 1 and 3 super-trials (p < 0.05) and 1–2 super-trials (p < 0.01), respectively. The symbols * and ** represent the mark of significant and very significant changed data.