Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study

Abstract

Neurofeedback training using electroencephalogram (EEG)-based brain–computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.

EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

“…The limited goodness‐of‐fit, even after fitting, suggests that the faster fMRI signal response may play a physiological role rather than a noise component. Moreover, recent EEG‐based neurofeedback studies have empirically implemented the extraction of relatively slow components of the EEG‐band power during feedback metric calculation to enhance the signal‐to‐noise ratio of the feedback signal (Hayashi et al, 2020 ; He et al, 2020 ; Kober et al, 2018 ; Kodama et al, 2023 ). Hence, the extraction of a slower component could elicit a component that reflects the excitability of the target region, which is beneficial for reconstructing the hemodynamic activities, while there needs an extension of dataset size to test the generalizability of the proposed architecture and results of middle layer analysis.…”

“…Band powers were calculated using a short-time Fourier transform with 1-s sliding windows with a 90% overlap. Subsequently, a third-order Butterworth low-pass filter (0.35 Hz) was applied to the band-power time course to improve the signal-to-noise ratio and avoid signal flickering, which would hamper the voluntary control of the SMR (Hayashi et al, 2020(Hayashi et al, , 2022He et al, 2020;Kober et al, 2018;Kodama et al, 2023). expected to exhibit less inter-subject variability than normalization using T1-weighted structural images (Calhoun et al, 2017).…”

“…Therefore, our observation of spatial weights in a trained network is a putative reflection of the relationship of the functional network that characterizes the activity of local brain regions. to-noise ratio of the feedback signal (Hayashi et al, 2020;He et al, 2020;Kober et al, 2018;Kodama et al, 2023). Hence, the extraction of a slower component could elicit a component that reflects the excitability of the target region, which is beneficial for reconstructing the hemodynamic activities, while there needs an extension of dataset size to test the generalizability of the proposed architecture and results of middle layer analysis.…”

“…Neurofeedback reinforces the targeted neural activity patterns by providing online feedback or rewards based on neural signals (Lawrence et al, 2014 ; Lubianiker et al, 2022 ; Naros & Gharabaghi, 2015 ; Shibata et al, 2019 ). A closed‐loop system, referred to as a brain–computer interface (BCI), is employed in the neurofeedback paradigm, actuating machines when targeted neural activity is detected (Biasiucci et al, 2018 ; Kodama et al, 2023 ; Pichiorri et al, 2015 ; Tsuchimoto et al, 2019 ). This experimental framework has paved the way for causally testing the relationship between targeted neural activities and behavioral performance because repetitive use of BCI modulates neural signals in the region of interest (Bagherzadeh et al, 2020 ; Iwama, Yanagisawa, et al, 2022 ; Sitaram et al, 2017 ).…”

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

“…Participants underwent 5 sessions of motor imagery task. The dataset is the reanalysis of [41] and the detailed procedures were reported in the article. In addition, we analyzed EEG dataset from the other research group to further investigate the generalizability of the proposed algorithm.…”