OpenMEA: Open-Source Microelectrode Array Platform for Bioelectronic Interfacing
Bibliographic record
Abstract
Abstract Bioelectronic interfaces have the potential to revolutionize the treatment of medical disorders and augment physiology. Implantable devices such as pacemakers and deep brain stimulators have already been deployed to control activity in diseases including Parkinson’s disease and epilepsy. These devices typically operate by delivering electrical stimulation at pre-programmed intervals (known as open-loop stimulation). Recent advances in machine learning and low-power integrated circuits have led to the emergence of personalized medical devices that monitor the user’s state and stimulate in response to measured biological activity (known as closed-loop stimulation). There are two key questions that require fundamental research to achieve breakthroughs in personalized devices: 1) What biomarkers and algorithms are best suited to detecting biological states (e.g. seizures in epilepsy)? and 2) What types of electrical stimuli are optimal for controlling these states? The answer to these questions can be explored in vitro using multielectrode array (MEA) systems that interface with biological tissue with reduced experimental complexity, better reproducibility, and fewer confounding variables present in whole organisms. However, existing MEA systems have functional limitations and closed-source designs that prevent researchers from developing improvements. This paper introduces OpenMEA, an open-source platform for closed-loop bioelectronics research. OpenMEA includes designs for the components necessary to build a benchtop in vitro laboratory, including electrophysiological recording and stimulation electronics, a microfluidic perfusion system, and physical designs for multielectrode arrays. The system is demonstrated with the electrical recording and stimulation of epileptogenic human and rodent brain slices. The aim of OpenMEA is to democratize bioelectronic research tools to accelerate the deployment of devices for the treatment of disorders and beyond.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".