Construction of a fiber-optically connected MEG hyperscanning system for recording brain activity during real-time communication
Bibliographic record
Abstract
Communication is one of the most important abilities in human society, which makes clarification of brain functions that underlie communication of great importance to cognitive neuroscience. To investigate the rapidly changing cortical-level brain activity underlying communication, a hyperscanning system with both high temporal and spatial resolution is extremely desirable. The modality of magnetoencephalography (MEG) would be ideal, but MEG hyperscanning systems suitable for communication studies remain rare. Here, we report the establishment of an MEG hyperscanning system that is optimized for natural, real-time, face-to-face communication between two adults in sitting positions. Two MEG systems, which are installed 500m away from each other, were directly connected with fiber optic cables. The number of intermediate devices was minimized, enabling transmission of trigger and auditory signals with almost no delay (1.95-3.90 μs and 3 ms, respectively). Additionally, video signals were transmitted at the lowest latency ever reported (60-100 ms). We furthermore verified the function of an auditory delay line to synchronize the audio with the video signals. This system is thus optimized for natural face-to-face communication, and additionally, music-based communication which requires higher temporal accuracy is also possible via audio-only transmission. Owing to the high temporal and spatial resolution of MEG, our system offers a unique advantage over existing hyperscanning modalities of EEG, fNIRS, or fMRI. It provides novel neuroscientific methodology to investigate communication and other forms of social interaction, and could potentially aid in the development of novel medications or interventions for communication disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".