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Decoding motor responses from the EEG during altered states of consciousness induced by propofol

Overview of attention for article published in Journal of Neural Engineering, February 2016
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Article details
Title
Decoding motor responses from the EEG during altered states of consciousness induced by propofol
Published in
Journal of Neural Engineering, February 2016
DOI 10.1088/1741-2560/13/2/026014
Pubmed ID
Authors
Abstract

Patients undergoing general anesthesia may awaken and become aware of the surgical procedure. Due to neuromuscular blocking agents, patients could be conscious yet unable to move. Using brain-computer interface (BCI) technology, it may be possible to detect movement attempts from the EEG. However, it is unknown how an anesthetic influences the brain response to motor tasks. We tested the offline classification performance of a movement-based BCI in 12 healthy subjects at two effect-site concentrations of propofol. For each subject a second classifier was trained on the subject's data obtained before sedation, then tested on the data obtained during sedation ('transfer classification'). At concentration 0.5 μg ml(-1), despite an overall propofol EEG effect, the mean single trial classification accuracy was 85% (95% CI 81%-89%), and 83% (79%-88%) for the transfer classification. At 1.0 μg ml(-1), the accuracies were 81% (76%-86%), and 72% (66%-79%), respectively. At the highest propofol concentration for four subjects, unlike the remaining subjects, the movement-related brain response had been largely diminished, and the transfer classification accuracy was not significantly above chance. These subjects showed a slower and more erratic task response, indicating an altered state of consciousness distinct from that of the other subjects. The results show the potential of using a BCI to detect intra-operative awareness and justify further development of this paradigm. At the same time, the relationship between motor responses and consciousness and its clinical relevance for intraoperative awareness requires further investigation.

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Geographical breakdown

Geographical breakdown
Country Count As %
Korea, Republic of 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 7 14%
Student > Master 5 10%
Student > Postgraduate 4 8%
Researcher 3 6%
Other 2 4%
Other 9 18%
Unknown 20 40%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 9 18%
Neuroscience 7 14%
Psychology 5 10%
Engineering 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 3 6%
Unknown 21 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 February 2016.
All research outputs
#19,155,287
of 27,776,330 outputs
Outputs from Journal of Neural Engineering
#1,288
of 1,865 outputs
Outputs of similar age
#260,998
of 417,162 outputs
Outputs of similar age from Journal of Neural Engineering
#24
of 35 outputs
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