Abstract
Brain–computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40–60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4–40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8–13 Hz), beta (14–30 Hz) and gamma (30–40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24–40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.
Highlights
Brain–computer interfaces (BCI) have been developed rapidly over the recent years, and especially their clinical application in neurorehabilitation has gained interest [1]
We examine the neurophysiological changes in healthy subjects performing motor imagery (MI)-BCI tasks with two different feedback modalities
We evaluated neurophysiological changes related to MI-BCI training with two different feedback modalities
Summary
Two MEG datasets involving MI-BCI experiments were included in this study In both cases, MEG was recorded at the MEG Core of Aalto University School of Science with a 306-channel Elekta NeuromagTM Vectorview system (MEGIN Oy, Helsinki, Finland) using a sampling frequency of 1 kHz and passband of 0.1–330 Hz. Five head-position indicator (HPI) coils were attached to the subject’s scalp for head position estimation and alignment with a standard coordinate system. The buffer was read over a local network connection by the stimulus computer (Intel Core i7-4771 CPU at 3.5 GHz, 64-bit Ubuntu Linux, version 12.04-LTS in Data 1 and version 14.04 in Data 2) This computer processed the MEG data in real time using functions implemented in the MNE-Python software [40], controlled the proprioceptive stimulators (in Data 2) and presented the visual stimuli using PsychoPy version 1.83 [41]
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