Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp
Bibliographic record
Abstract
Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and Internet of Things. Conventional electroencephalography relies on wet and dry electrodes. Despite favorable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes, and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Herein, a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite is reported. The CMSA sensor is fabricated using the innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".