Median Nerve Stimulation Based BCI: A New Approach to Detect Intraoperative Awareness During General Anesthesia

Abstract

Hundreds of millions of general anesthesia are performed each year on patients all over the world. Among these patients, 0.1-0.2% are victims of Accidental Awareness during General Anesthesia (AAGA), i.e., an unexpected awakening during a surgical procedure under general anesthesia. Although anesthesiologists try to closely monitor patients using various techniques to prevent this terrifying phenomenon, there is currently no efficient solution to accurately detect its occurrence. We propose the conception of an innovative passive brain-computer interface (BCI) based on an intention of movement to prevent AAGA. Indeed, patients typically try to move to alert the medical staff during an AAGA, only to discover that they are unable to. First, we examine the challenges of such a BCI, i.e., the lack of a trigger to facilitate when to look for an intention to move, as well as the necessity for a high classification accuracy. Then, we present a solution that incorporates Median Nerve Stimulation (MNS). We investigate the specific modulations that MNS causes in the motor cortex and confirm that they can be altered by an intention of movement. Finally, we perform experiments on 16 healthy participants to assess whether an MI-based BCI using MNS is able to generate high classification accuracies. Our results show that MNS may provide a foundation for an innovative BCI that would allow the detection of AAGA.

Keywords: anesthesia; brain-computer interface; intraoperative awareness; median nerve stimulation; motor imagery.

Figure 1

(A) Illustration of the timings and amplitudes of the desynchronization and the followed synchronization induced by a real movement, a motor imagery, and a median nerve stimulation according to Salenius et al. (1997),Schnitzler et al. (1997), and Neuper and Pfurtscheller (2001) in the mu and beta frequency bands. (B) Illustration of the expected timing and amplitudes of the desynchronization and the followed synchronization induced by a median nerve stimulation during a motor imagery according to Salenius et al. (1997),Schnitzler et al. (1997),Neuper and Pfurtscheller (2001), and Kilavik et al. (2013) in the mu and beta frequency bands. The time scale is not precisely detailed.

Figure 2

A healthy voluntary subject is lying on a comfortable chair with his eyes closed. His legs rest on a footrest and his right forearm rests on a cushion to prevent movement. The OpenViBE software records 128 EEG electrodes and delivers starting and stopping beeps and stimulations of the median nerve when necessary according to the experimental conditions. The subject physically or mentally presses and releases a remote button. The operator displays the EEG signals during the experiment.

Figure 3

Representation scheme for one trial. Timing schemes of a trial for C1, C2, C3, and C4. For all motor tasks, one low frequency beep indicates when to start the task. For the MNS+MI condition, the MNS occurs at 750 ms after the first beep. The end of the MI is announced by a high frequency beep and followed by a rest period of 6 s.

Figure 4

Time-frequency grand average analysis (ERSP) for Real movement, Motor Imagery, Motor Imagery + MNS, and MNS conditions for electrode C₃. A black line indicates when the motor task started and finished. A flash picture indicates when the median nerve stimulation started. A red color corresponds to a strong ERS and a blue one to a strong ERD. Significant difference (p < 0.05) are shown in the final part of the figure.

Figure 5

Topographic map of ERD/ERS% (grand average, n = 16) in the alpha/mu+beta band during two conditions: MI + MNS and MNS only. A red color corresponds to a strong ERS and a blue one to a strong ERD. A black line indicates when the motor imagery started or finished for the MI + MNS condition. Red electrodes indicate a significant difference between the two conditions (p < 0.05).

Figure 6

Grand average (n = 16) ERD/ERS% curves in the mu (7–13 Hz), the beta (15–30 Hz), and the mu+beta (8–30 Hz) bands for MI (in violet), MI + MNS (in blue), and MNS (in red) conditions for electrode C₃. The yellow bar at 750 ms corresponds to the median nerve stimulation performed. For the MI and MI + MNS conditions, the MI starts at 0 s and ends at 2 s.

Figure 7

Grand average accuracies obtained by 4 differents classifiers (MDM, CSP + LDA, CSP + MDM, TS + LR) for the 3 conditions (RM, MI and MI + MNS) in the mu + beta band (8–30 Hz).

Figure 8

Boxplots showing the distribution of average classification accuracies (n = 16) for MI vs Rest and MI + MNS vs. MNS class. ^***p-value < 0.001.

Figure 9

Accuracies obtained for all subjects (n = 16) by TS + LR analyses in the 8–30 Hz for the 3 conditions (RM, MI and MI + MNS).

Figure 10

Average performances and standard deviation for thee classification tasks: MI+MNS vs. MNS, RM vs. Rest, and MI vs. Rest. The three first bars show the results obtained for the 7–130, 15–30, and 8–30 Hz frequency bands. The fourth bar labeled “Personalized bands” is the average and standard deviation of results when the best frequency band for each subject is chosen, i.e., the frequency band yielding the highest performance. Statistical significances are displayed as well, obtained in a student's t-test. The classifier is the TS+LR, described in section 2.9. ^*p-value < 0.05, ^**p-value < 0.01, ^***p-value < 0.001.