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

Twelve right-handed healthy volunteers (aged 18–28, 5 females) participated in this study. None had any known neurological or motor impairments, nor contraindications for the use of propofol. All participants gave written informed consent prior to the experiment. Measurements took place in an operating room at the Radboud University Medical Centre in Nijmegen, the Netherlands.

All procedures were according to the Declaration of Helsinki and were approved by the local Medical Ethics Committee.

The experiment consisted of three blocks. The first experimental block was a baseline block in which the subjects performed the movement tasks without administration of propofol (block 0). In the subsequent blocks, propofol was administered via a target controlled infusion (TCI) pump in steps of 0.5 μg ml⁻¹ (target concentration). So, the target concentration was 0.5 μg ml⁻¹ for the second block (block 0.5) and 1.0 μg ml⁻¹ for the third block (block 1.0). An Alaris PK infusion pump (Carefusion, Basingstoke, UK) was used in the TCI mode (Schnider model, effect-site targeting). When the target concentration had been reached, participants waited for another 10 min before proceeding with the experiment, to ensure equilibration between the body compartments. Only subjects S1, S2 and S3 received an additional propofol dose increase with 1.5 μg ml⁻¹ as target concentration. During the entire procedure heart rate, blood pressure and oxygen saturation (pulse oximetry) were monitored. After the end of the experiment participants remained in the OR complex until they were fully recovered.

In each block, sequences of nine movement trials were presented to the subjects. Each trial consisted of an auditory 3 s cue, with a 4 s silence interval between consecutive trials. At the start of each sequence, an auditory instruction was given explaining the task for the upcoming trials: either 'move' (continuous hand tapping) or 'do not move'. The participants had to perform the instructed task during the auditory cues, and rest during the silence intervals (figure 1). Participants were asked to keep their eyes closed throughout the entire sequence. Between sequences participants could have a short rest, then start the next sequence by pressing a button. Per block, between 54 and 63 trials were presented for each of the two task conditions. Within each block, presentation of the sequences was randomized. A short practice block to get the participants acquainted with the task preceded the actual measurements.

Zoom In Zoom Out Reset image size

The experiment was programmed in and run on the BrainStream platform⁴ Version 1.0, i.e. a Matlab (MathWorks Inc., MA, USA) toolbox especially developed for online BCI-experiments, using Psychtoolbox⁵ for stimulus presentation.

EEG was recorded with a 32-channel actiCAP system (Brain Products), based on the international 10/20 system. Impedances were kept below 25 kΩ before starting the measurement, and the sampling rate during recording was 2500 Hz. After recording, signals were downsampled to 128 Hz.

Two electrodes were removed from the EEG cap and instead used to record the left forearm electromyogram (EMG). Muscle outputs as recorded by EMG were used to determine if and when participants had executed the wrong task. EMG signals were rereferenced using a bipolar reference for the two channels and high-pass filtered at 10 Hz to reduce the effect of artifacts such as electrode drift. Then, the signals were converted to power over time by taking the absolute magnitude of the analytic signal as found using a Hilbert transform, and the mean power per subject and movement condition was determined for the period between 0.1 and 3.5 s (task onset is at 0). Trials for which the EMG power deviated more than 3 times the standard deviation from the mean for that subject and condition were excluded from further analysis. For the remaining trials, the mean amplitude per subject per condition was determined, as well as the mean movement onset time and standard deviation for the movement tasks by identifying the first rising edge of the EMG amplitude increase.

Additionally, Bispectral index (BIS) was measured using the Philips M1034AX (BIS) Solution plug-in module (Philips Medical Systems, Eindhoven, The Netherlands). BIS is a commercial depth of anesthesia monitor, providing a number between 0 (no brain activity) and 100 (completely awake) [16]. While no true gold standard is currently available, BIS is generally considered to be one of the most important monitors of anesthetic depth in clinical use and it is relatively well known among anesthesiologists. Its straightforward output gave an indication, both during the experiment and when interpreting the results, about the overall awareness reduction in our participants. Values were recorded manually every 1–2 min during the experimental blocks.

2.3.1. Classification

2.3.2. Statistical analyses

This study showed that motor responses could be detected from the EEG of volunteers during altered states of consciousness, with an average single trial classification accuracy of 85% at a propofol effect-site concentration of 0.5 μg ml⁻¹ and 81% at a propofol effect-site concentration of 1.0 μg ml⁻¹. Single trial 'transfer' classification accuracies of 83% and 72% were obtained at propofol effect-site concentrations of 0.5 and 1.0 μg ml⁻¹, respectively. Adding this to previous findings showing the possibility of detecting attempted movement during neuromuscular block [13], we conclude that further development of the proposed BCI is justified. During various conditions of drug administration, including both hypnotics (propofol) and neuromuscular blocking agents (rocuronium), movement can be distinguished from rest with high accuracy.

For eight of the 12 subjects it was possible to 'transfer' between the baseline state and the highest level of sedation. In other words, a classifier trained on the subjects' data obtained prior to administering propofol was able to detect the movements at the target concentration of 1.0 μg ml⁻¹ with an accuracy above chance level (p < 0.05). This fact is useful for subsequent steps in development of the paradigm, specifically for determining the most efficient way of system calibration. The transfer of the BCI from a baseline condition to the sedation conditions is relevant because of the inter- and intrasubject variabilities in the brain signal. Currently, most BCIs require a calibration phase for each individual user and session. Especially in the developmental phase of BCI paradigms, system calibration is an essential step. In the near future however, end-user applications may no longer require this phase, as novel methods are being developed in which a generic classifier can be applied to every user's data. This means there is not only a transfer between different states in an individual user, but also between users. Promising results have recently been reported on such so-called zero-training BCIs for a spelling paradigm [25], and also movement-related BCIs may be feasible without (or with very limited) user-specific calibration [26–29].

The successful transfer was partly based on adequate compensation of propofol-induced effects. Most conventional EEG-monitors in anesthesia, like BIS or entropy module, use only frontal electrodes to detect drug-induced EEG changes. However, hypnotic drugs like propofol have substantial effects on the EEG measured at other electrode locations as well [30]. Accordingly, we found a clear propofol effect with a β-increase at the central electrodes, where the main EEG effect of (attempted) movement is located. By baselining each individual trial this increase was cancelled out. As a result, the classifier only took into account relative motor response effects. Because the exact change of the background EEG does not have to be known, this crucial compensation seems to be a large benefit of using a BCI algorithm during general anesthesia. However, the baselining procedure may introduce other issues, as discussed further on.

Unexpectedly, for four subjects (33%), a classifier trained on the baseline data could not distinguish between movement and rest at a propofol effect-site concentration of 1.0 μg ml⁻¹. If these subjects were excluded, the transfer classification performance would increase from 72% to 79% (95% CI 74%–85%). Studying the properties of these trials may shed light on how propofol-induced sedation interferes with sensorimotor integration. However, for a full understanding, future studies will be required that directly target at addressing these issues. Nevertheless, careful analysis of the available data showed that a few patterns emerged. Speculating on the reason for the low classification performance for these four subjects, we point out a few aspects.

First, visual inspection of the individual time–frequency spectra of these subjects revealed a large reduction or even absence of the ERD/ERS pattern at the effect-site concentration of 1.0 μg ml⁻¹. Second, the individual differences in the propofol-induced β-increase should be considered. For S2, the largest relative increase in power between 14 and 22 Hz was found (power at 1.0 μg ml⁻¹ target concentration was more than 200% of the power at 0.0 μg ml⁻¹ target concentration). A large increase was also seen in S1 and S3, but the same was true for S7, S10 and S11 (all between 150% and 190%). While overall the performance was largely increased by adding the baselining procedure, in some cases this may in fact have attenuated the ERD/ERS response altogether. Third, remarkably, the first three subjects entering the study all belonged to the group of four subjects with deviating results. They were the only participants in whom we attained the highest propofol concentration, i.e. 1.5 μg ml⁻¹. At this effect-site concentration, all three subjects became unable to follow commands. Afterwards, none of these subjects exhibited recall of events from after administration of this third and final dose. Fourth, the four subjects for whom the transfer classification performed below chance level were the four who had the largest increase in both mean movement onset times as well as the largest spread between said onset times. For two of these subjects, S2 and S6, the mean EMG amplitude at 1.0 μg ml⁻¹ target concentration was less than 50% of the amplitude at 0.0 μg ml⁻¹ target concentration, which may point to a change in task intention (note that a reduction or absence of motor output itself does not necessarily mean a reduced brain response; the intention itself seems to be the most important factor [13]). Fifth, S1, S2 and S3 had made relatively many errors in executing the movement task as compared to the remaining subjects at 1.0μg/ml target concentration, while S6 had made the most mistakes at 0.5 μg ml⁻¹ target concentration. This may indicate a misinterpretation of sensory information and therefore a lower level of awareness and command following. Finally, the mean BIS value at 1.0 μg ml⁻¹ target concentration was lower for these four subjects (80.6) than the mean of the other eight subjects (84.4).

The current study was based on the assumption that a patient under general anesthesia would either be unconscious and hence not move while being stimulated, or the patient would be conscious and move, unless paralysed. Our findings indicate that instead a more detailed model of consciousness should be adopted.

While the above observations may not be sufficient to draw any hard conclusions, they do indicate that the group of four volunteers may have been close to a state described by Pandit [31] as 'dysanesthesia' at the target concentration of 1.0 μg/ml. In this state, one may respond to simple commands but not to surgical stimuli. There is a certain degree of consciousness, but perception and sensory input are uncoupled such that memory formation is unlikely. This state is, according to Pandit, the minimum requirement for satisfactory general anesthesia [32]. Our findings seem to be in line with the functional model of consciousness proposed by Pandit as well as his view on the isolated forearm technique (IFT, [33, 34]).

While reviewing results from IFT, Pandit has suggested that patients may have retained some limited capacity for responsiveness to simple command, but that this does not always mean consciousness. The method of IFT is simple but its interpretation is controversial. One arm of the patient is isolated from the circulation so that it remains unaffected when a neuromuscular blocker is administered. The patient is asked to respond to command by moving the unparalysed hand. An awake patient would thus be able to communicate his/her state of awareness. According to the Global Workspace Theory specialized neuronal networks can execute movements on verbal command without the subject being conscious. This could explain why in some IFT studies patients responded to command, but did not show any spontaneous response to surgery [31]. In addition, movements during IFT are not correlated with any recall of events in most of the studies on IFT [35].

It might be hypothesized that in our study the movements were executed on a subconscious level at a certain propofol target concentration. For the four volunteers showing—to a certain extent—deviating results, the progressive loss of the respective functions constituting consciousness could have been more rapid than in the other participants. If Pandits model is right and if the group of four shows signs of dysanesthesia, then they would no longer belong to the primary target population of our research project.

As the BCI detects real cortical involvement during movement, it may be an even better measure of intraoperative awareness than the BIS or the IFT. Nevertheless, we must recognize that the state of dysanesthesia might represent a precursor for awareness [32].

To find more conclusive answers on the matters discussed here, future studies on our proposed paradigm could be expanded with behavioral measures to track memory formation after drug administration. Moreover, it would be highly interesting to measure the EEG above the motor cortex during the IFT and to correlate this with memory formation. A difficulty presented by that type of research setting, however, is that it is very sensitive to the Heisenberg uncertainty principle with a marked observer effect: every behavioural measurement of consciousness itself is potentially altering the state of consciousness by being an arousal stimulus.

Despite this study being conducted in an operating room, the question still remains to which extent this controlled research setting mirrors the real clinical situation. The focus of a patient awakening during surgery who follows movement commands for seconds to escape this situation is different from that of a paid volunteer performing a non-demanding repeating task in a non-stimulating environment for more than an hour. This may explain the marked sedation of the volunteers at relatively low propofol concentrations, as the actual state of sedation is always the result of the balance between sedating drug effect and environmental arousal. For example, Röpcke and colleagues [36] studied the effect of surgical stimulation on the EEG. They found a shifted dose-response relationship for the effect of anesthetic drugs on the EEG depending on the presence or absence of surgical stimulation.

This study was a first exploration as to whether motor response detection after hypnotics administration may be feasible. Meanwhile, BCI research is continually bringing forth further advancements, of which many may be applied to the paradigm proposed here. For example, improvement of motor detection performance could be gained by taking into account multi-trial classification (i.e. increasing the amount of information the classifier uses for making its decision) and by adapting the system to achieve a certain true positive/false positive trade-off rate. Moreover, with the development of more advanced EEG systems a reduction in setup time and signal noise can be expected in the near future. The introduction of wireless and dry electrodes will also mean an improvement in comfort and user-friendliness, which is important for clinical use [37–39].

To conclude, despite a clear effect of propofol on the EEG, changes in sensorimotor rhythms could still be detected in sedated volunteers. These findings are encouraging for the further development of a BCI for detecting attempted movements during intraoperative awareness. Importantly, in contrast to existing monitors, it is based on active communication by the patient, rather than a passive interpretation of the brain signal. However, alongside further technical development of the proposed system, a more precise model of the relationship between motor responses and consciousness is required. Because some volunteers moved without a clear correlated EEG response, future studies are needed to investigate the exact state of consciousness in these cases, as well as its clinical relevance for intraoperative awareness. This provides an opportunity for deeper insights in this challenging field of research. At the very least, anesthesiology research could benefit from BCI technology in general. Much insight could be gained by connecting with this field of research as anesthesiologists and BCI experts pursue a similar goal: communication by patients in a conscious locked-in state.