Spatial Resolution of Phosphenes within the Visual Field using Non-Invasive Transcranial Alternating Current Stimulation
Bibliographic record
Abstract
Abstract Non-Invasive Transcranial Alternating Current Stimulation (NITACS) is a method that applies weak electrical currents to the scalp or face to modulate brain activity. A fascinating application of NITACS is the induction of phosphenes — visual phenomena where individuals perceive light without external stimuli. These phosphenes have been observed and generated through various techniques, including direct electrical stimulation of the visual cortex. However, NITACS provides a non-invasive way to create these visual effects. This research aimed to understand the spatial resolution of NITACS-induced phosphenes, vital for visual aid technology and neuroscience. Eight healthy participants underwent NITACS with a novel electrode configuration on the face. Findings indicated that NITACS could induce phosphenes that showed spatially defined patterns in the visual field. The phosphene locations differed among participants but were consistently within the visual field. These patterns remained stable across repeated stimulations. Optimal parameters were determined for inducing vibrant phosphenes without discomfort. The study also identified electrode positions that moved phosphenes to various visual field regions. Receiver Operating Characteristics (ROC) analysis estimated specificity and sensitivity at 70.7% and 73.9%, respectively, with a control trial effectiveness of 98.4%. Overall, NITACS holds promise as a reliable non-invasive means to modulate visual perception.
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 | 0.001 |
| 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 | 0.000 |
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".