Joint recording of EEG and audio signals in hyperscanning and pseudo-hyperscanning experiments
Bibliographic record
Abstract
Hyperscanning is an emerging technique that allows for the study of brain similarities between interacting individuals. This methodology has powerful implications for understanding the neural basis of joint actions, such as conversation; however, it also demands precise time-locking between the different brain recordings and sensory stimulation. Such precise timing, nevertheless, is often difficult to achieve. Recording auditory stimuli jointly with the ongoing high temporal resolution neurophysiological signal presents an effective way to control timing asynchronies offline between the digital trigger sent by the stimulation program and the actual onset of the auditory stimulus delivered to participants via speakers/headphones. This configuration is particularly challenging in hyperscanning setups due to the general increased complexity of the methodology. In other designs using the related technique of pseudo-hyperscanning, combined brain-auditory recordings are also a highly desirable feature, since reliable offline synchronization can be performed by using the shared audio signal. Here, we describe two hardware configurations wherein the real-time delivered auditory stimulus is recorded jointly with ongoing electroencephalographic (EEG) recordings. Specifically, we describe and provide customized implementations for joint EEG-audio recording in hyperscanning and pseudo-hyperscanning paradigms using hardware and software from Brain Products GmbH.•Joint EEG-audio recording configuration for hyperscanning and pseudo-hyperscanning paradigms.•Near zero-latency playback of auditory signal captured by a microphone.•Precise alignment between EEG and auditory stimulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".