LZc, SCE and ACE computed as averages over multiple 10sec segments of EEG of the 7 subjects before and during anaesthesia.

<p>States shown are wakeful rest (WR) before propofol, mild sedation (MS), LOC and wakeful rest emerging from propofol sedation (WRa, not shown for single subject results). Measures are computed across 25 channels spread evenly across the whole cortex. The measures score highest for shuffled WR data, and consistently across subjects higher for WR as opposed to LOC. Error bars indicate standard error across segments, cyan horizontal lines are example thresholds for each of the measures, separating WR from LOC for all 7 subjects. For each single subject plot, the mean and standard error across its 7 values per state is displayed in the narrow plot to its right, with the title ‘mean’. For these mean values across subjects, significant differences between state pairs are shown by a double asterisk if <i>p</i> < 0.01 and a single asterisk if <i>p</i> < 0.05 (Wilcoxon rank sum test, FDR corrected for multiple comparison). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133532#pone.0133532.t001" target="_blank">Table 1</a> for effect size comparison.</p

Discriminative power across subjects of nine measures, as measured by area under the ROC curve (AROC).

<p>Each symbol represents a state pair as indicated. The AROC is computed from the mean scores for the 7 subjects, obtained from the broadband signal from 25 electrodes from the whole cortex. The closer the AROC is to 0 or 1, the better the measure is at discriminating the given state-pair, close to 1 signifying that the measure tends to be greater for the more conscious state and close to 0 signifying that the measure tends to be greater for the less conscious state. When the AROC is 0.5 there is no discriminative power; hit rate equals false alarm rate for all classification thresholds. LZc, ACE and SCE have nearly maximal discriminative power for state pairs LOC/MS and LOC/WR. The measure sumCov fails to discriminate LOC/MS yet has strong (inverse) discriminative power for LOC/WR and MS/WR. Normalized delta band power discriminates LOC/WR and LOC/MS strongly yet MS/WR poorly.</p

Analysis of 25 k-medoids chosen electrodes for each of four lobes.

<p>Blue star is WR, red diamond is LOC. The red error bars indicate standard error. LZc and ACE score for all subjects higher for WR as opposed to LOC, SCE does so for 6/7 subjects. The measures behave similarly for different brain regions. If the effect size Cohen’s <i>d</i> < 0.8 for the scores of a given subject, the subject’s label is printed in red.</p

Schematic of the computation of Lempel-Ziv complexity LZc.

<p>a) <i>x</i>_<i>i</i> is the activity of the <i>i</i>^<i>th</i> EEG electrode, i.e. the <i>i</i>^<i>th</i> channel, and <i>a</i>_<i>i</i> is the (Hilbert) amplitude of <i>x</i>_<i>i</i>. b) <i>b</i>_<i>i</i> is binarized <i>a</i>_<i>i</i>, using the mean activity of <i>a</i>_<i>i</i> as binarisation threshold. c) After binarisation of all <i>n</i> channels, successive <i>n</i> × 1 dimensional observations are concatenated to obtain one binary sequence <i>s</i> in which patterns are searched and listed into a dictionary of binary words via a Lempel-Ziv algorithm. d) Lempel-Ziv complexity LZc is proportional to the size of this dictionary.</p

Complexity measures for increasingly regular activation/synchrony matrices.

<p>Given a random binary matrix of activations (for ACE and LZc) or synchronies (for SCE), with increasing number of duplicated channels, the complexity measures monotonically decrease with the number of equal channels. See text for details.</p

Channel selection from the high density (256 electrodes) EEG.

<p>The electrode layout is from manufacturer EGI [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133532#pone.0133532.ref040" target="_blank">40</a>]. The homogeneously distributed channel selection is shown for: a) analyses across the whole scalp; b) analyses restricted to a certain lobe. Chosen channels are indicated as black stars. See text for details.</p

Effect size comparison per measure and frequency band for WR/LOC.

<p>For each measure and frequency-band-filtered input, the three numbers display how many subjects out of 7 had higher score for WR than LOC with Cohen’s <i>d</i> > 0.8 (left digit), no substantial difference, <i>d</i> < 0.8, between WR and LOC (middle), and lower values in WR when compared to LOC with Cohen’s <i>d</i> > 0.8 (right). High-pass-filtered input data are labelled by > 1<i>Hz</i>.</p><p>Effect size comparison per measure and frequency band for WR/LOC.</p

Pearson correlations between measures.

<p>Each black triangle indicates the positive correlation for the given measure pair listed in increasing order along the x-axis. The correlation was computed across subjects for the ratio of WR/LOC. Each correlation of all possible pairs of 9 measures was tested for significance and only those with 2-tailed <i>p</i>-value smaller than 0.05 are plotted. The red triangle indicates negative correlation. All 3 pairs out of ACE, LZc, SCE correlate significantly. There is no significant correlation of delta power with any other of the 9 measures for the state pair WR/LOC.</p

Two 10sec EEG segments from 25 channels.

<p>The segment in the left panel is during wakeful rest (WR) and the segment in the right panel is during propofol-induced loss of consciousness (LOC); both segments are shown after pre-processing. The voltage scale is the same for both conditions and the maximal fluctuation shown is approximately 0.1<i>mV</i> (for more data details, see [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133532#pone.0133532.ref016" target="_blank">16</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133532#pone.0133532.ref038" target="_blank">38</a>]). The recordings for LOC display visibly stronger slow waves (low-frequency components) as compared to those for WR.</p

Schematic of the computation of SCE.

<p>a) Two time series. b) The analytic signals of these two, being complex signals with the real part being the original signal and the imaginary part being the Hilbert transform of the signal. c) A synchrony time series is created for this pair of signals, being 1 at time <i>t</i> if the phases of the complex values of the analytic signals have similar magnitude at this time <i>t</i>. d) SCE^(<i>i</i>) with respect to channel <i>i</i> is the entropy over columns (<i>n</i> × 1 synchronies) of the matrix Ψ_<i>i</i> containing all <i>n</i> synchrony time series for channel <i>i</i>. The overall SCE is then the mean value of the SCE^(<i>i</i>) across channels.</p