Editorial: Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age
Bibliographic record
Abstract
Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age This editorial summarizes the contributions to the Frontiers Research Topic "Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age, " established under the Frontiers in Human Neuroscience (section: Brain-Computer Interfaces), Frontiers in Neuroergonomics (section: Consumer Neuroergonomics), and Frontiers in Neuroscience (section: Neural Technology) journals.Everyone is subject to the aging process and to experiences associated with physical and health changes, which include but are not limited to changes in memory and brain function.These changes may be debilitating due to increased dependence of people as they get older.Many engineering tools and neuroscience breakthroughs have been used to assist these individuals in their daily life activities or to enhance, restore, and improve the effects of the many age-related changes.Emerging technologies such as cognitive and motor control based on invasive and non-invasive brain-computer interfaces (BCIs) provide opportunities to improve the health and wellbeing of the elderly.These external approaches may assist elderly patients improve motor function (e.g., in stroke survivors) and induce and/or facilitate neuroplastic changes associated with motor rehabilitation, caretakers and family members to better communicate with these patients, and healthcare professionals to better monitor and detect changes in the patients' health status.While there has been great progress in the field of BCI over the last decades, the development of hardware and software solutions for home-based BCI applications that can be used by individuals with reduced technical oversight is still lacking.In addition, user-friendly, wearable, portable, and wireless BCI applications, as well as more longitudinal neuroscience studies are essential to understand how BCI applications can improve the quality of life of elderly people.This editorial paper will introduce the five papers appearing in this Research Topic.The papers touch on different aspects of BCI usage for improving the health and wellbeing in older age.As will be summarized, several innovations in bio-signal processing methods and decoding algorithms are introduced that ease the effect of aging on the human brain and enrich the mutual interplay between senior citizens and machines.With the objective of assisting in cognitive support,
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.019 |
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".