A Multifunctional, Embedded-based, Bluetooth-enabled, Programmable, Biphasic-waveform Stimulator with Real-time Neural Signal Acquisition
Bibliographic record
Abstract
The study introduces a lightweight, Bluetooth-enabled deep brain stimulation (DBS) device that is designed specifically for preclinical research in small, freely moving animals. To address the current limitations in waveform flexibility and wireless control, we developed a compact, multifunctional stimulator capable of delivering programmable, charge-balanced biphasic waveforms while simultaneously acquiring neural signals. The system integrates off-the-shelf components for current regulation, signal amplification, and analog-to-digital conversion, all managed by a low-power microcontroller. The assembly includes key steps such as circuit integration on a double-sided 30 x 30 mm PCB, waveform programming via nRF Connect, and validation through saline and load resistance testing. In vitro assessments demonstrated reliable current output across varying load impedances, effective saline operation without waveform degradation, and high-fidelity neural signal recording with a signal-to-noise ratio exceeding 35 dB. These results confirm the device's suitability for closed-loop neuromodulation experiments and lay the groundwork for future translational studies in DBS therapy.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".