Summary: Patients surviving severe traumatic brain injuries often enter states designated as Prolonged Disorders of Consciousness (PDoC) or Locked-In Syndrome (LIS). For decades, standard bedside clinical diagnostic assessments have relied entirely on observable motor responses—such as tracking an object with the eyes, reflexive flinches, or simple physical command following.
However, if a neural injury completely disconnects the motor cortex from the musculature, a fully aware, conscious mind can remain entirely trapped inside an unresponsive body. Historical estimates suggest that up to 40% of patients categorized as minimally conscious are misdiagnosed as entirely unaware due to these physical evaluation boundaries.
To bridge this diagnostic gap, researchers developed a wearable Brain-Computer Interface (BCI) system that successfully detects patterns of intentional brain activity without requiring physical movement. The study introduces a structured, multi-session framework that provides real-time auditory neurofeedback to patients. By evaluating 42 participants across multiple clinical sites, the team discovered that repeated training sessions dramatically strengthen the clarity of covert cognitive signals, nearly doubling the clinical detection rate of minimally conscious states from 39% to 69% and opening an eventual path toward basic non-verbal communication.
Key Facts
- The BCI Paradigm Shift: While historical BCI trials evaluated patients in a single testing session, the University of Bath study introduces a longitudinal, multi-session training framework designed to help damaged brains actively learn to modulate their signals over time.
- Motor Imagery Detection: The wearable EEG headset records internal electrical rhythms, capturing distinct, intentional shifts in brain activity when an unresponsive patient simply imagines a motor action (e.g., lifting weights with the left hand or lifting both feet).
- Auditory Neurofeedback Loops: Patients receive immediate, real-time sound confirmations the moment the BCI algorithm captures an intentional motor imagery signature. This neurofeedback allows patients to systematically refine their internal mental strategies, making their brain waves sharper and more consistent across sequential sessions.
- Staged Cognitive Questioning: Approximately 90% of participants who demonstrated reliable brainwave modulation advanced to a staged questioning phase, where they were trained to utilize distinct motor imagery signals to answer basic binary “yes” or “no” exploratory prompts.
- Quantitative Diagnostic Gains: When combined with traditional behavior checklists, the multi-session BCI framework elevated the clinical detection rate of minimal consciousness from 39% to 69%, unmasking hidden awareness that traditional bedside exams missed entirely.
- Deployment Versatility: Bypassing dense, immobile laboratory machinery, this lightweight, portable BCI architecture is engineered to run seamlessly within active, real-world clinical environments, long-term care homes, or domestic residences.
Source: University of Bath
A new approach for identifying signs of hidden awareness in people who cannot speak or move after severe brain injury has been demonstrated by researchers at the University of Bath in the UK.
The system detects patterns of brain activity through a wearable headset using an advanced application of brain–computer interface (BCI) technology.
Across multiple experimental sessions, the researchers have uncovered signs of consciousness that were previously undetected in unresponsive patients.
This represents a potential advance in diagnostic methods and rehabilitation planning for patients. It also offers promising possibilities for future technologies that may help patients communicate without the use of voice or movement.
Published in the Nature journal Communications Medicine, the study was carried out in patients with prolonged disorders of consciousness (PDoC) and locked-in syndrome (LIS) – conditions in which awareness may be preserved but cannot be outwardly expressed.
By recording brainwaves in these patients, this BCI technology detects when a person imagines a hand or arm movement, even when no physical movement is possible. Detection accuracy can improve if neurofeedback is provided over multiple sessions.
Structured approach
The study introduced a structured multi-session approach to assessing awareness in patients, combining:
- Repeated training, where participants were taught to intentionally change their brain signals over time. Study participants were asked to imagine actions such as lifting a weight with their left hand or lifting both feet. This led to distinct patterns of brain activity that could be detected, even in the absence of any physical movement, and translated into meaningful signals.
- Real-time feedback, where participants received immediate sound-based feedback, confirming to them that the system had detected the correct pattern of imagined movement.This real-time feedback has the potential to help participants refine their mental strategies across sessions, with their brain responses becoming clearer and more consistent over time – in a manner similar to how an uninjured person learns a physical or cognitive skill. Importantly, the researchers found that repeated, structured evaluations across multiple sessions was found to improve the detection of awareness in, and engagement from, unresponsive patients.
- Staged questioning, where participants progressed to an exploratory yes–no question phase (for instance, they were asked to imagine one movement to indicate ‘yes’ and a different movement to indicate ‘no’).These questions were structured to explore different types of mental capacity and awareness.
Study outcomes
The protocol was tested in 42 participants aged between 17 and 73 years, recruited across multiple NHS and Irish clinical sites.
Key findings included:
- 31 of 42 participants (73.8%) showed reliable intentional modulation of brain activity – i.e. consistent patterns or rhythms in the signals – when asked to imagine specific movements.
- Approximately 90% of those participants progressed to the phase of the study designed to elicit yes-no responses.
- Brain responses often became more consistent across sessions.
- When used alongside standard behavioural tests, the multi session BCI approach improved detection of minimal conscious state from 39% to 69%, helping identify awareness that might otherwise go unnoticed.
Why behavioural tests alone are not enough
Standard bedside assessments rely heavily on observable movement such as eye movements, reflexes and simple command following. However, when injuries are severe enough to prevent physical movement, these assessments can substantially underestimate awareness.
Previous research suggests that up to 40% of patients in a minimally conscious state may be misdiagnosed as ‘awake but unaware’ because signs of cognitive activity are missed.
Brain-based assessments offer a means to detect purposeful responses even when a person cannot express themselves behaviourally. While earlier studies have demonstrated that a single assessment could reveal covert awareness, a single session represents only a minimal assessment window.
The researchers of the new study anticipated that such signals could be trained over time through repeated assessment where feedback was also provided, potentially leading to stronger evidence that consistently points to the presence of awareness and consciousness.
Lead author Dr Naomi du Bois, a researcher at the Institute for the Augmented Human (IAH) at the University of Bath, said: “This work shows how brain based response to structured questions could complement bedside assessment and help clinicians detect hidden awareness earlier.”
Senior author Professor Damien CoyleInstitute for the Augmented Human director of the IAH and a researcher in the Department of Computer Science at Bath, said: “The novelty here is the move beyond single-session assessment. We’ve shown that a structured multi-session BCI framework, with training, feedback and staged questioning, can operate in real clinical environments or in the home or care home, and strengthen the reliability of detecting signs of awareness.
“This creates a pathway toward improved diagnosis and may ultimately support patients to interact and communicate basic responses in some cases.”
Key Questions Answered:
A: Traditional tests rely almost entirely on visible, physical proof—doctors look to see if a patient can squeeze a hand, blink on command, or follow a moving object with their eyes. However, severe brain trauma can fracture the structural pathways that connect a perfectly conscious, thinking mind to the body’s physical muscles. The patient might hear and understand the command perfectly, but they are physically “locked in” and incapable of generating a motor response. Because the baseline exam only measures the body’s physical execution rather than the brain’s internal intent, covert awareness goes completely unnoticed.
A: The secret lies in the system’s real-time auditory feedback loop. In older BCI experiments, a patient’s brain waves were recorded in a single session with zero interaction, which is incredibly difficult for a damaged brain. The University of Bath system turns the assessment into an active learning process. When a patient imagines a movement, like lifting their left hand, the headset listens for that specific neural rhythm. The absolute millisecond it detects the correct pattern, it plays a distinct sound. This immediate feedback lets the patient realize, “Yes, that mental strategy worked,” allowing them to practice and sharpen that thought pattern over multiple sessions just like someone learning a new physical skill.
A: The ultimate milestone is to transition this technology out of elite research labs and deploy it directly into hospitals, care facilities, and private homes as a permanent communication tool. By moving from simple motor imagery to “staged questioning,” researchers have already shown that patients can learn to associate one imagined movement with “yes” and another with “no.” Professor Damien Coyle emphasizes that this creates a highly accessible pathway to give completely non-verbal, paralyzed individuals a reliable, non-invasive voice, allowing them to communicate basic needs, express comfort levels, and actively participate in their own long-term medical care.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neurotech research news
Author: Vittoria D’Alessio
Source: University of Bath
Contact: Vittoria D’Alessio – University of Bath
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness” by 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 & Damien Coyle. Communications Medicine
DOI:10.1186/s43856-026-01574-x
Abstract
Advancing EEG-based assessment of consciousness and cognition in prolonged disorders of consciousness
Background
Accurate assessment of residual awareness in patients with Prolonged Disorders of Consciousness (PDoC) remains a major clinical challenge, as conventional behavioural tools can underestimate covert cognition. This study evaluates whether a structured, multi-phase motor imagery Brain–Computer Interface (MI-BCI) protocol provides objective electroencephalography (EEG)-based indicators of awareness that complement behavioural assessments.
Methods
Forty-four participants (N = 44) completed repeated imagined-movement tasks using wearable EEG (PDoC: Unresponsive Wakefulness Syndrome (UWS, n = 14), Minimally Conscious State (MCS, n = 17), Locked-In Syndrome (LIS, n = 11); two able-bodied participants as benchmarks; ClinicalTrials.gov: NCT03827187; 30-01-2019). The protocol assessed sensorimotor rhythm modulation, training with and without neurofeedback, and binary question answering across phases. Standard behavioural assessments (CRS-R and WHIM) were administered at each session.
Results
Significant MI-BCI decoding accuracy (DA) is achieved by 73.8% of patients, of whom 90% progress to Q&A testing and frequently exceed the 70% usability threshold, revealing marked inter-individual heterogeneity. For significant MI-BCI runs, LIS outperform MCS (p = 0.007) and UWS (p = 0.048), while UWS exceed MCS during Q&A (p = 0.049), driven by familiar-voice stimuli. Using leave-one-subject-out cross-validation, combining predictions from DA and behavioural assessments improves balanced diagnostic accuracy to 62% (from 55%), increasing sensitivity to MCS (39% to 69%), with a modest reduction in LIS sensitivity (78% to 67%). Task-related activity over sensorimotor and parietal cortices differentiate diagnostic groups.
Conclusions
The structured MI-BCI protocol demonstrates potential as a movement-independent, EEG-based tool for distinguishing UWS, MCS and LIS. Integrating DA and spatial patterns yields diagnostic information that may augment behavioural assessment and advance objective tools for evaluating awareness in PDoC.
