Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training

Abstract

Background: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.

Objective: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).

Methods: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.

Results: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p = 0.021 for healthy people; p = 0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p = 0.099 for healthy people, p = 0.084 for people with stroke).

Conclusions: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.

Keywords: Brain–computer interfaces; Event-related design; Feedback; Motor imagery; Motor learning; Near-infrared spectroscopy.

Fig. 1

Conceptual diagram of paradigms and comparison groups (PT: passive training, AT: active training, EEG: electroencephalogram, fNIRS: functional near infrared spectroscopy)

Fig. 2

Schematic representation of the experimental protocol. (a) Experimental protocol; yellow boxes indicate protocols for people with a stroke (b) Experimental setting (c) Diagrammatic illustration of the placement of the EEG electrodes and the fNIRS optodes [43]

Fig. 3

Conceptual diagram of different datasets for data analysis

Fig. 4

Averaged ERSP map and topographic distribution maps across all the healthy participants (dB scale). True feedback indicated the trial sets with robotic feedback for task periods, whereas no feedback indicated the trial sets without robotic feedback for task periods

Fig. 5

Results of the analysis of hemodynamic response between paradigms in the region of interest (ROI) channel for all study participants (PT: passive training, AT: active training). The data represent the mean values of each index, where the indices from 30 trials per participant were averaged. (a, b) Boxplots for the peak oxyhemoglobin (oxy-Hb) between each paradigm; *indicate statistically significant differences (p < 0.05) for the normalized peak oxy-Hb in (a) healthy volunteers and (b) patients with stroke. (c, d) Comparison of grand mean across all participants and statistical results for oxy-Hb concentrations between each two paradigms; for (c) healthy volunteers and (d) patients with stroke (red solid lines: Raw paradigm; purple solid lines: Match paradigm; black dash-dot lines: AT; brown dash-dot lines: PT). The gray-shaded area indicates a statistically significant difference in the concentrations between two different paradigms (*p < 0.05)

Fig. 6

Results of the analysis of hemodynamic responses in all channels. (a, b) Topographic distribution of the average oxyhemoglobin (oxy-Hb) for different paradigms in (a) healthy participants and (b) patients with stroke (PT: passive training, AT: active training) (c, d) Topographical distribution of results of statistical comparison between all paradigms and between Match and Raw paradigms for (c) healthy volunteers and (d) patients with stroke. The color axis indicates a − log₁₀(p-value); channels marked with an asterisk have a p-value < 0.05

Fig. 7

Results of the analysis of hemodynamic response between the Raw+, Raw–, and Match paradigms in the region of interest (ROI) channel for study participants. The data represent the mean values of each index, calculated by averaging the indices from all trials per participant (a, b) Boxplots for normalized peak oxyhemoglobin (oxy-Hb) between the three paradigms for (a) healthy volunteers and (b) patients with stroke; *indicate statistically significant differences (p < 0.05). (c, d) Comparison of grand mean and time sample data for oxy-Hb concentrations between two paradigms for (c) healthy volunteers and (d) patients with stroke (dark red solid lines: Raw+; pink solid lines: Raw–; purple solid lines: Match paradigm). The gray-shaded area indicates a statistically significant difference in the concentrations between the two paradigms (*p < 0.05)

Fig. 8

Results of the comparison between the Raw+, Raw–, and Match paradigms for all channels. (a, b) Topographic distribution for different paradigms from (a) healthy volunteers and (b) patients with stroke (c, d) Topographical distribution of results of statistical analysis between the three paradigms for (c) healthy volunteers and (d) patients with stroke. The color axis indicates the -log₁₀(p-value); the channels marked with an asterisk showed statistically significant differences (p < 0.05)