Carbon Nanotube Polymer Composite Based Electrodes with Ability to Adhere to Hairy Skin for Application in Electrophysiological Sensing
Bibliographic record
Abstract
Biopotential recordings such as electroencephalogram (EEG) and electrocardiogram (ECG) measurements have applications in medical diagnosis, health care, human-machine interface, entertainment, etc. Electrodes are an important part of biopotential acquisition systems affecting the quality of recorded signals, comfort to the user, and the cost. Many conventional electrodes are either expensive or disposable, many are rigid and non-conformal to the skin, and some cause irritation and allergic reaction due to the use of aggressive chemical adhesives. Further, Silver/Silver Chloride (Ag/AgCl) electrodes are the only medical grade electrodes available for EEG measurements from the scalp. They are placed using EEG hats and a conductive gel is filled between the electrode and the scalp. Sometimes this gel leaks out causing shorting between consecutive electrodes. Such systems consume time to place on the head, requires assistance from experts, and restricts the user movements. Non-medical grade dry electrodes are used as an alternative to Ag/AgCl electrode setups for EEG measurements, but they require mechanical support to attach, and therefore, they are susceptible to motion artifacts. Hence, there is a need for a soft wearable electrode that is cost-effective, reusable, and can adhere to the skin regardless of the level of hair coverage and topology of the skin. This thesis presents a novel soft reusable carbon-nanotube-polymer composite based electrode capable of biopotential recording from a high dense hairy area such as the scalp. The electrode consists of an array of tulip-like microstructures that utilizes suction force in combination with the use of trace amount of medical-grade conductive gel to achieve sufficient adhesion force and conformability to hairy skin for biopotential recording. A novel low-cost scalable fabrication process was developed, and mathematical and experimental analysis of capillary rise that plays a key role in the optimization of the fabrication process was carried out. The proposed electrode was fabricated and electrophysiological signal measurements were performed. The electrode can adhere to the skin conformably resulting in low electrode-skin interface impedance and good signal to noise ratio. The electrode is found to be very comfortable to the users and capable of recording ECG and EEG for an extended amount of time.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".