An experimental study of the effect of neuromuscular blockade on EEG-based measures of awareness

Many EEG-based measures of consciousness, such as spectral slope, Lempel-Ziv complexity, connectivity, alpha peak frequency, and power in canonical frequency bands, fail to detect awareness in awake individuals who are paralyzed by neuromuscular blocking agents. In a study of six healthy volunteers, these measures distinguished awake-unparalysed states from sedated-paralysed states with near-perfect accuracy, but misclassified 7% to 100% of time segments from awake-paralysed subjects as unaware. The findings highlight critical limitations of current EEG-based measures for detecting awareness, particularly in clinical settings where muscle relaxants are used.

Study at a glance

Abstract

Both basic and clinical consciousness research aims to find objective measures that reliably distinguish conscious from unconscious brain states. Electroencephalogram (EEG) measures are widely used, although they may be affected by interference from electrical signals such as those generated by muscles. To assess this source of error, we investigated the impact of neuromuscular blockade (NMB) on proposed measures of awareness (spectral slope, Lempel-Ziv complexity (LZc), connectivity, alpha peak frequency, power in canonical EEG frequency bands) computed from spontaneous high-density EEG recorded from six healthy volunteers in three different conditions: (1) awake-unparalysed (normal wakefulness), (2) awake-paralysed (complete paralysis caused by neuromuscular blocking agent (NMBA)), and (3) sedated-paralysed (deep sedation with propofol, with paralysis by NMBA). The measures we investigated distinguished awake-unparalysed states from sedated-paralysed with close to perfect accuracy in accordance with past findings. However, our analysis revealed a serious failure of most measures to recognise the awake-paralysed condition as an aware state. Errors ranged from 7% of awake-paralysed time segments predicted as unaware (using alpha power) to 100% (using LZc). Using a unique high-density EEG data set, this study clearly demonstrates that many EEG-based measures fail to recognise awareness in awake subjects under the influence of muscle relaxants. These results highlight critical limitations of current EEG-based measures at detecting awareness.