Implementing EEG hyperscanning setups
Bibliographic record
Abstract
Hyperscanning refers to obtaining simultaneous neural recordings from more than one person (Montage et al., 2002 [1]), that can be used to study interactive situations. In particular, hyperscanning with Electroencephalography (EEG) is becoming increasingly popular since it allows researchers to explore the interactive brain with a high temporal resolution. Notably, there is a 40-year gap between the first instance that simultaneous measurement of EEG activity was mentioned in the literature (Duane and Behrendt, 1965 [2]), and the first actual description of an EEG hyperscanning setup being implemented (Babiloni et al., 2006 [3]). To date, specific EEG hyperscanning devices have not yet been developed and EEG hyperscanning setups are not usually described with sufficient detail to be easily reproduced. Here, we offer a step-by-step description of solutions to many of these technological challenges. Specifically, we describe and provide customized implementations of EEG hyperscanning setups using hardware and software from different companies: Brain Products, ANT, EGI, and BioSemi. •Necessary details to set up a functioning EEG hyperscanning protocol are provided.•The setups allow independent measures and measures of synchronization between the signals of two different brains.•Individual electrical Ground and Reference is obtained in all discussed systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".