Significance Anesthesiologists reversibly manipulate the brain function of nearly 60,000 patients each day, but brain-state monitoring is not an accepted practice in anesthesia care because markers that reliably track changes in level of consciousness under general anesthesia have yet to be identified. We found specific behavioral and electrophysiological changes that mark the transition between consciousness and unconsciousness induced by propofol, one of the most commonly used anesthetic drugs. Our results provide insights into the mechanisms of propofol-induced unconsciousness and establish EEG signatures of this brain state that could be used to monitor the brain activity of patients receiving general anesthesia.

Time and frequency domain analyses of scalp EEG recordings are widely used to track changes in brain states under general anesthesia. Although these analyses have suggested that different spatial patterns are associated with changes in the state of general anesthesia, the extent to which these patterns are spatially coordinated has not been systematically characterized. Global coherence, the ratio of the largest eigenvalue to the sum of the eigenvalues of the cross-spectral matrix at a given frequency and time, has been used to analyze the spatiotemporal dynamics of multivariate time-series. Using 64-lead EEG recorded from human subjects receiving computer-controlled infusions of the anesthetic propofol, we used surface Laplacian referencing combined with spectral and global coherence analyses to track the spatiotemporal dynamics of the brain's anesthetic state. During unconsciousness the spectrograms in the frontal leads showed increasing α (8-12 Hz) and δ power (0-4 Hz) and in the occipital leads δ power greater than α power. The global coherence detected strong coordinated α activity in the occipital leads in the awake state that shifted to the frontal leads during unconsciousness. It revealed a lack of coordinated δ activity during both the awake and unconscious states. Although strong frontal power during general anesthesia-induced unconsciousness-termed anteriorization-is well known, its possible association with strong α range global coherence suggests highly coordinated spatial activity. Our findings suggest that combined spectral and global coherence analyses may offer a new approach to tracking brain states under general anesthesia.alpha rhythm | delta rhythm | loss of consciousness T ime-domain and frequency displays of continuous surface EEG recordings have been used for many years to track changes in the state of the brain under general anesthesia (1-7). Although these analyses have shown that different spatial patterns appear over the scalp as the state of general anesthesia changes, the extent to which these patterns are spatially coordinated and may indicate interesting dynamics in the underlying brain networks has not been systematically characterized. Several experimental and analysis issues must be addressed to carry out this characterization. First, analysis of this coordinated activity with an appropriate level of spatial resolution requires use of high-density (≥64 leads) EEG recordings (8). With few exceptions, EEG studies of general anesthesia use no more than 20 electrodes (9). Second, although in principle high-density EEG recordings should provide higher resolution information about the spatial structure in brain activity under general anesthesia, the increased amount of data creates the analysis challenge of determining how to make informative temporal assessments of the spatial dynamics in these multivariate time-series. Analyzing the cross-spectral matrix for a range of relevant frequencies as a function of time would offer a way to conduct a temporal analysis of coordinated spatial...

Rhythmicoscillationsshapecorticaldynamicsduringactivebehavior,sleep,andgeneralanesthesia.Cross-frequencyphase-amplitudecoupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions. One state occurs during unconsciousness and may be similar to sleep slow oscillations. A second state occurs at the loss or recovery of consciousness and resembles an enhanced slow cortical potential. These results provide objective electrophysiological landmarks of distinct unconscious brain states, and could be used to help improve EEG-based monitoring for general anesthesia.

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.

Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a "plug-and-play" fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers. Keywords Human-robot interaction • EMG control • EEG control • Hybrid control • Gesture detection • Error-related potentials • Plug-and-play supervisory control This is one of the several papers published in Autonomous Robots comprising the Special Issue on Robotics: Science and Systems.