A decoded neurofeedback method for MEG/SSVEF

• ¹ Research Institute of National Rehabilitation Center for Persons with Disabilities, Department of Rehabilitation for Brain Function, Japan
• ² The University of Electro-Communications, Brain Science Inspired Life Support Research Center, Japan

Decoded neurofeedback method was recently proposed to lead brain activity to a target state (Shibata, et al., 2011). While researchers utilized functional magnetic resonance imaging in the former studies, little is focused on magnetoencephalography (MEG). Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience, and in this study, we used an rtMEG system to perform decoded neurofeedback training for MEG/steady-state visual evoked field (SSVEF). Five able-bodied participants (age 33.2 years old, 5 females) participated in this study. The visual stimuli were displayed on a screen in front of the participant, and consisted of a circular checkerboard patch on the left and a circular checkerboard patch on the right. The checkerboard patches flickered at 5 (left) or 6 (right) Hz. They were asked to attend to the left, right, or middle of the screen in a SSVEF task. The used MEG scanner was a 306-channel Elekta Neuromag system (Elekta Oy, Helsinki, Finland). We constructed a sparse multinomial logistic regression (SMLR) decoder from MEG signals during the SSVEF task in each participant. We then conducted 3-day MEG neurofeedback training. In a trial, a white fixation cross was turned green for 5 second, then a green solid circle, whose radius indicated a score of the SMLR decoder, was presented. Participants were asked to “somehow regulate your brain activity to make the green solid circle bigger while the fixation cross is green.” During the trainings, no flickering visual stimuli were presented. After the trainings, the SSVEF task was used again to evaluate the training effects. Furthermore, we analyzed weights of the SMLR decoders to determine a subset of MEG sensors that contributed to decode the attended orientation. The SMLR decoder was able to classify the MEG signals into 3 attentional directions (left, right or middle) (88.2 %). The participants were able to increase decoder scores through the trainings (p<0.05). In the post-training SSVEF tasks, accuracy for the target orientation was higher than that for the non-target orientation (p<0.05). Analysis of weights of the decoders demonstrated that sensors on the occipital region contributed to decode the attended orientation (p<0.05, Bonferroni corrected). The results suggest that the decoded neurofeedback training was effective for MEG/SSVEF, and the method may enhance robustness of steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI).