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A Familiar Voice Woke Up Brain Signals in ‘Unresponsive’ Patients

In A Nutshell

  • A wearable EEG headset detected brain signals that may point to hidden awareness in patients thought to be unconscious.
  • Nearly three-quarters of 42 patients produced brain-signal patterns above chance during initial testing.
  • Patients labeled “unresponsive” showed stronger brain signals when questions were read in a familiar caregiver’s voice.
  • Combining brain-signal data with standard behavioral tests raised diagnostic accuracy from 55% to about 62% in early testing.

Certain patients lying completely still in hospital beds, apparently unaware of the world around them, may actually be processing words and registering sounds, with no physical way to show it. A new study tested whether a lightweight, wireless headset worn on the scalp could detect brain signals that may point to hidden awareness or command following. The results were compelling enough to matter for how doctors diagnose and treat some of the most vulnerable patients in medicine.

Published in Communications Medicine, the study tested a brain-computer interface system on 44 participants: 42 patients, plus two healthy adults included as a baseline, across hospitals, care homes, and private homes in the United Kingdom and Ireland. Patients fell into three diagnostic categories linked to severe brain injury or serious neurological illness, including people with locked-in syndrome, a condition in which a person is fully conscious but almost completely paralyzed. Nearly three-quarters of patients produced brain-signal patterns above chance during the initial assessment phase, suggesting many could follow the task mentally even when bedside exams might not capture it.

This matters enormously because doctors currently diagnose patients with severe brain injuries largely by watching for physical responses: eye movements, flinching, following a finger. When those responses are absent or inconsistent, patients can be wrongly labeled as unaware, a mistake that affects life-and-death decisions about care. Studies estimate that up to 40% of patients in what’s called a “minimally conscious state” are misdiagnosed as being in a lower state of awareness. Reading brain signals directly, without needing any physical movement, could help fix that.

Wearable Brain Sensor Detects Signals of Intentional Thought

Researchers recruited 42 patients aged 17 to 73, split into three groups: 14 with unresponsive wakefulness syndrome, appearing awake but showing no signs of awareness; 17 with minimally conscious state, showing some flickering awareness; and 11 with locked-in syndrome. Each participant wore a wireless headset with 16 small sensors pressed against the scalp that recorded tiny electrical signals produced by the brain.

Patients moved through three phases across up to 13 total sessions, each lasting one to two hours. Participants imagined making specific arm or foot movements, for example lifting a weight with the right arm, while the system tried to decode which movement they were imagining. Because imagining movement activates the same brain regions as actually performing it, the pattern of electrical signals can reveal whether someone is following instructions mentally, even if their body stays completely still. Later sessions added real-time audio feedback and, in the final phase, yes-or-no questions covering biographical details, basic logic, numbers, and everyday situations. Patients answered by imagining one of two movements to signal ‘yes’ or ‘no.’ Those questions were recorded in the voice of a familiar caregiver or family member, meant to maximize emotional engagement.

A middle-aged male patient in a coma in a hospital bed.
New brain-computer tech may catch awareness doctors miss in patients with severe brain injuries. (Credit: metamorworks/Shutterstock)

Familiar Voices Boost Brain Signals in Unresponsive Patients

Of the 42 patients, 73.8% passed the initial assessment phase, and 90% of those went on to complete the question-and-answer phase. Patients with locked-in syndrome generally produced the cleanest, most easily decoded brain signals, which makes sense, since their consciousness is intact. But patients previously labeled as “unresponsive” also showed detectable signals, and some performed surprisingly well.

One of the more telling findings involved patients classified as unresponsive during the question-and-answer phase. This group showed a notable boost in brain-signal quality during those sessions compared to earlier training sessions. Researchers suggest the familiar voice may have triggered deeper cognitive engagement, consistent with prior evidence that personally meaningful sounds can activate brain networks even in patients with very low awareness. The paper stops short of claiming this unlocked hidden awareness; the researchers describe the interpretation as theoretical and in need of further testing.

Brain Sensor Data Improves Diagnostic Accuracy Over Bedside Tests

When researchers combined brain-signal data with scores from two standard behavioral tests, diagnostic accuracy improved from 55% to about 62%. They tested this by training the system on most patients, then checking it against the ones left out, a way of estimating how it might work on a new patient. These numbers come from a research exercise, not a real hospital rollout, so how the combined system performs in everyday care still needs confirming. The gain was most pronounced for patients in the minimally conscious state, whose correct identification improved from roughly 38% to 69%.

Beyond the accuracy numbers, the headset’s biggest advantage is that it does not require a patient to move, blink, or speak to register a response. Unlike hospital scanners such as fMRI machines, available only in specialized facilities and requiring patients to be transported, this headset can be brought directly to a patient’s home or care facility, making it far more practical as a real-world tool.

Authors are candid about what still needs work. Group sizes were unequal, and confidence intervals around the diagnostic estimates were wide, meaning the true benefit of combining brain-signal data with behavioral tests could be larger or smaller than observed. Testing across hospitals, care homes, and private homes introduced variability that a controlled lab setting would avoid, and the headset’s 16 sensors provide limited detail about exactly where in the brain activity is occurring.

Still, for families who have sat beside a loved one wondering if they can hear or understand what’s happening around them, a headset that can detect the flicker of an answer is something genuinely new. The idea is no longer science fiction, though it still needs more testing before it can guide routine care.


Disclaimer: This article is based on peer-reviewed research and is intended for general informational purposes only. It is not medical advice. Anyone with questions about the diagnosis or care of a loved one with a disorder of consciousness should speak with a qualified healthcare professional.


Paper Notes

Limitations

The study’s authors identify several important limitations. Group sizes were unequal across the three patient categories, which constrained some statistical comparisons, and confidence intervals around the diagnostic accuracy estimates were wide, reflecting uncertainty in the findings. The use of a 16-channel EEG headset limited spatial resolution, making it difficult to pinpoint precisely where in the brain activity was occurring; the authors describe the brain-network connectivity analyses as exploratory. Sessions were conducted across hospitals, care homes, and private residences, introducing variability in testing environments and session timing. Patients’ fluctuating alertness, medication effects, and the inherent instability of their conditions complicated standardization. The authors also flag a circularity concern: because participants were initially diagnosed using behavioral assessments, the very tools the study aims to supplement, future work should blind researchers to diagnoses and remove the assessment threshold used to advance participants between phases, to avoid sampling bias. Concurrent muscle-movement monitoring was not recorded, though visual inspection did not reveal sustained artifacts consistent with muscle activity. The exploratory analysis of question-level accuracy for individual cases was based on a snapshot sample of one participant per group and should be interpreted cautiously.

Funding and Disclosures

The study was supported by internal funds from the University of Bath and Ulster University, by access to High Performance Computing resources provided by the Northern Ireland High Performance Computing facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under Grant number EP/T022175, and by the UK Research and Innovation (UKRI) Turing AI Fellowship 2021 to 2025 funded by the EPSRC under Grant number EP/V025724/1. One researcher was supported by the NIHR Sheffield Biomedical Research Centre and NIHR Sheffield Clinical Research Facility. The authors note that the views expressed are their own and not necessarily those of the NHS, the NIHR, the National Rehabilitation Hospital, or the Department of Health and Social Care. The corresponding author, Damien Coyle, is disclosed as the founder, Chief Executive Officer, and a shareholder of NeuroCONCISE Ltd., a company involved in the development of neurotechnology and wearable EEG systems. Manuscript editing was aided by ChatGPT (OpenAI), with all content reviewed and verified by the authors.

Publication Details

Paper Title: Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness | Authors: Naomi du Bois, Attila Korik, Stephanie Hodge, Leah Hudson, Ainjila S. Elahi, Alain Bigirimana, Natalie Dayan, Jose M. Sanchez-Bornot, Alison McCann, Kudret Yelden, Lloyd Bradley, Krishnan P.S. Nair, Simon Judge, Damon Hoad, Emma Vines, Venu Harilal, Sheryl Parke, Paul Johnson, Jacqueline Pogue, Emma Dodds, Abayomi Salawu, Raymond Carson, Karl McCreadie, Jacqueline Stow, Jacinta McElligott, Aine Carroll, and Damien Coyle. | Corresponding Author: Damien Coyle ([email protected]), Bath Institute for the Augmented Human, University of Bath, Bath, UK, and Intelligent Systems Research Centre, Ulster University, Derry, UK. | Journal: Communications Medicine (a Nature Portfolio journal) | Volume/Issue: Volume 6, Article 344 (2026) | DOI: https://doi.org/10.1038/s43856-026-01574-x | Clinical Trial Registration: ClinicalTrials.gov, NCT03827187, registered January 30, 2019.

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