Editorial: Ear-centered sensing: from sensing principles to research and clinical devices, volume II
Bibliographic record
Abstract
Dedicated sensor and amplifier technology are paramount for successful long-term usage of earcentered sensing devices. Authors that contributed their papers in this Special Topic have made significant strides in this area, striving for unobtrusiveness in every aspect. From biocompatible materials that adapt to individual anatomy to lightweight, inconspicuous instrumentation, their innovations promise to enable seamless data acquisition without restricting users' daily activities.In this volume II of our Research Topic, we present a diverse range of research articles that collectively advance the field of ear-centered sensing. We have compiled a brief overview of each article to pique your interest:This study explores the potential of ear-centered EEG to monitor auditory attention during continuous speech, offering insights into assistive devices for complex auditory environments.Investigating the feasibility of real-time endogenous ear-EEG-based braincomputer interfaces, this article explores the potential of ear-EEG in online environments.In-Ear Electro-Oculography for Attended Speaker Estimation": Utilizing in-ear electrooculography, this research focuses on improving comprehension in hearing-impaired individuals during conversations, with implications for hearing assistive devices.Electrode Grid for Self-Applied EEG Sleep Monitoring at Home": Addressing the need for convenient home sleep monitoring, this article presents a self-applicable EEG sensor array for accurate sleep evaluation.Investigating the potential of ear-EEG to determine levels of attention and focus, this study explores the integration of ear-EEG into wearable devices for monitoring mental load.This article assesses the alignment of audio and EEG data in the context of hearing aid algorithms, offering insights into future closed-loop EEG and audio applications.Exploring sound localization in children with hearing conditions, this study highlights the benefits of non-surgical bone conduction devices.Introducing a generic ear-EEG device for at-home sleep monitoring, this research emphasizes the potential for widespread sleep stage monitoring.Artifacts": This study establishes the sensitivity of ear-EEG to neural sources and ocular artifacts, supporting its integration into EEG paradigms.As you delve into these nine articles, we hope you gain a deeper appreciation for the transformative potential of ear-centered sensing across various domains, from healthcare to cognitive science. By bringing together experts from different disciplines, we aim to foster collaboration and innovation in this every-day growing field.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".