Amazing stuff!
"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 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 possibilities for future technologies that may help patients communicate without the use of voice or movement. ..."
"... 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.
..."
From the abstract:
"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."
Using brain technology, awareness is detected in unresponsive patients " (original news release) A new approach using repeated assessments to identify signs of hidden awareness in people who cannot speak or move after brain injury has been demonstrated."
Fig. 4: Decoding accuracy (DA) across runs for all participants.
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