Progress Report

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Integration of Asian humanities and brain informatics to enhance peace and compassion of the mind[2] Neurofeedback

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Progress until FY2025

1. Outline of the project

In this project, we are developing technology aimed at creating a new society where machines coexist with humans. To achieve this goal, we have developed visualization devices capable of sufficiently capturing the transition of mental states. Furthermore, by utilizing this neurofeedback system, we aim for participants to acquire a balance of dynamism and stability in brain networks, unaffected by external influences.

Fig.1
Figure 1: Schematic diagram of this R&D proposal

2. Outcome so far

“Realtime detection of EEG state-dynamics”

EEG microstates are an EEG analysis technique that has recently regained attention. By first extracting common EEG templates that capture the spatiotemporally continuous dynamics, brain state transitions can be represented as transitions between these templates. We have previously developed a system for real-time detection of state transitions corresponding to vertical and horizontal rotations in a spherical EEG state space (Figure 2).

Fig.2
Figure 2: Real-time detection and feedback of state transitions corresponding to vertical and horizontal rotations in a spherical state space

Among participants who underwent neurofeedback training using the system we developed, repeated training was found to modulate the plasticity of EEG states toward the intended direction (Figure 3), and no adverse events were observed.

Fig.3
Figure 3: Learning effect by using neurofeedback system
“Machine Learning of Brain State Transitions”

Using a multi-task EEG dataset, we extracted five source-localized attractors with a hidden Markov model. These attractors are highly replicable both spatially and temporally. Resting state was characterized by relatively high frequencies of the two attractors on the left side and low frequencies of the middle attractor.

Fig.4
Figure 4: Five highly replicable, source-localized attractors of EEG

3. Future plans

Based on the achievements obtained up to this fiscal year, we found that training with the EEG neurofeedback system we developed to induce brain-state transitions can improve the ability to achieve brain states in a specific direction. We also identified attractors associated with resting state, which might be useful when training “the peace of mind” with neurofeedback. Going forward, it will be necessary not only to clarify what positive effects can be obtained through the continued application of this type of neurofeedback training, but also to develop strategies for implementing it in society. By integrating these findings, we aim to establish a practical neurofeedback framework that promotes mental well-being and, ultimately, to generate outcomes that can be broadly returned to society.

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