Abstract
BackgroundIn 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 MethodsUsing 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.ResultsResults 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).ConclusionThe 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.
Highlights
Changes in ERD’s Mu-suppression score (MuSC) can be observed in short-term motor imagery (MI) experiments with feedback, but the trend of the data is subtle and difficult to use to measure the effect of short-term training
We focused on using EEG signals to investigate what impact MI-BrainComputer Interface (BCI) training can have on the brain in short-term
This paper is a preliminary attempt in the field of EEG brain network-based rehabilitation assessment
Summary
Electroencephalograph (EEG)-based BCI systems is often applied in combination with motor imagery (MI) paradigm (Alkadhi et al, 2005) for neurorehabilitation training (Kumar et al, 2016; Baig et al, 2017; Oikonomou et al, 2017; Cheng et al, 2018), especially for enhancing motor recovery from brain injuries such as stroke hemiplegia (Buch et al, 2008; ZimmermannSchlatter et al, 2008; Daly et al, 2009; Langhorne et al, 2009; Barclay et al, 2020). Motor Imagery (MI) based BrainComputer 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). We use the electroencephalogram (EEG) signals to explore the brain patterns’ changes after a short-term rehabilitation training
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