Bibliographic record
Abstract
The past few years have seen the availability of consumer electroencephalography (EEG) devices increase significantly. These devices, usually with a compact form factor and affordable price, now allow researchers and enthusiasts to use EEG in various new contexts and environments. However, the many consumer headsets often require extensive programming experience to interface with their respective drivers; moreover, standardization of the access and recording of EEG data across the devices still remains to be done. Consequently, a tool is needed to facilitate the recording and streaming of EEG data from consumer headsets that can easily be interfaced with different programming languages and software, and that allows interchangeability between devices. This paper describes the open source MuSAE Lab EEG Server (MuLES), an EEG acquisition and streaming server that aims at creating a standard interface for portable EEG headsets, in order to accelerate the development of brain-computer interfaces (BCIs) and of general EEG use in novel contexts. In addition to the EEG server interface which currently supports five different consumer devices and session playback, clients are developed for use on different platforms and in various programming languages, making prototyping and recording a quick and simple task. To validate the functionality and usability of the EEG server, a use case highlighting its main features is presented. The developed tool simplifies the acquisition and recording of EEG data from portable consumer devices by providing a single efficient interface, with applications in areas such as basic and behavioural research, prototyping, neurogaming, live performance and arts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".