Towards a novel monitor of intraoperative awareness: Selecting paradigm settings for a movement-based brain-computer interface

Contains fulltext : 103039.pdf (publisher's version ) (Open Access)During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.9 p

Stimulus sequence.

<p>Each sequence started with an auditory sequence instruction, i.e. ‘No Movement’ or ‘Both Arms Movement’. Following the instruction were nine trials consisting of a cue (task) and their corresponding baseline periods used for analysis.</p

Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.

<p>Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.</p

Cumulative probability of true positive monitor output after start of movement.

<p>Movement was assumed to start at trial 1 (t = 0 s) with a trial duration of 8 seconds. As in this paradigm 4 positive classifications in a row are needed for a positive monitor output, the first possible positive monitor output is at trial 4 i.e. 32 seconds after movement onset. For each subject plus the average of all subjects, the solid line shows the output for the recorded sequences. The dashed lines show the interpolation of this output for another 9 trials using a Monte Carlo simulation.</p

Single trial classification rates per subject using different frequency ranges.

<p>Calculations were done using 9 channels and the combined ERD and ERS periods.</p

Single trial classification rates per subject using different frequency resolutions.

<p>Calculations were done using 9 channels and the combined ERD and ERS periods.</p

Decrease in average classification rate with corresponding standard errors when reducing the number of trials.

<p>Information was used from either t = 0–4 s (ERD), t = 4–6 s (ERS) or both (ERD+ERS). Channel sets correspond to the channel sets in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044336#pone-0044336-g002" target="_blank">Figure 2</a>. To clearly show the rates for all time periods, data points are slightly shifted to either left or right. The dashed line represents the binomial confidence interval, i.e. all classification rates above this line are significantly better than chance (p = 0.01).</p

Grand average time-frequency plot of all 64 channels.

<p>Spatial downsampling was performed with a Surface Laplacian; the period from t = −1.5 to t = −0.5 s was used as the baseline period. Blue colouring represents ERD; red represents ERS. The motor cortex is situated in the central regions (C3–C4). An enlargement of channel C3 is shown in the right-hand corner, with the dashed lines indicating the onset and offset of the auditory cue (task period).</p

EEG Electrode positions used for analysis.

<p>The full set of 64 electrodes was used for computation of the grand average time-frequency plot. The remaining channel sets consisted of 18, 12, 9 and 6 channels. The 4-channel set denotes the Laplacian C3.</p

Symptoms and quality of life before, during, and after a SARS-CoV-2 PCR positive or negative test:data from Lifelines

This study evaluates to what extent symptoms are present before, during, and after a positive SARS-CoV-2 polymerase chain reaction (PCR) test, and to evaluate how the symptom burden and quality of Life (QoL) compares to those with a negative PCR test. Participants from the Dutch Lifelines COVID-19 Cohort Study filled-out as of March 2020 weekly, later bi-weekly and monthly, questions about demographics, COVID-19 diagnosis and severity, QoL, and symptoms. The study population included those with one positive or negative PCR test who filled out two questionnaires before and after the test, resulting in 996 SARS-CoV-2 PCR positive and 3978 negative participants. Nearly all symptoms were more often reported after a positive test versus the period before the test (p &lt; 0.05), except fever. A higher symptom prevalence after versus before a test was also found for nearly all symptoms in negatives (p &lt; 0.05). Before the test, symptoms were already partly present and reporting of nearly all symptoms before did not differ between positives and negatives (p &gt; 0.05). QoL decreased around the test for positives and negatives, with a larger deterioration for positives. Not all symptoms after a positive SARS-CoV-2 PCR test might be attributable to the infection and symptoms were also common in negatives