Fig 1.
Schematic presentation of the set up with two speakers S1 and S2 and 12 listeners.
The listeners k = 1, 2⋯6 of each group L1−k and L2−k are instructed to follow the narration of a particular speaker, indicated by the heavy lines, while having the narration of the other speaker simultaneously accessible, shown by the thin lines.
Fig 2.
An illustration of higher-order structures (cliques) corresponding to the indicated topology level q.
Fig 3.
Mapping multi-brain correlations.
(a) Maximum topological dimension plotted against various thresholds w0 (top panel) stabilises near the selected threshold 0.06. The middle part of the histogram of correlation coefficients averaged over 882 channels (lower panel); the fit by the normal distribution, dotted line, deviates from the data for the correlations larger than 0.06. (See the histogram for the whole range in Fig A(b) in S1 File). (b) The central part of the histograms for different channels. For a particular channel, the correlations with other 881 channels are marked by the corresponding colour indicated in the colour map; the presence of colours over different bins suggests that all channels obey a similar distribution. (c) An example of higher-order structure involving the signal locations at two scalps—the speaker’s S2 and the listener’s L2−3. (d) The adjacency matrix of the multi-brain network of the two speakers and 12 listeners with the threshold w0 = 0.06. The order of indexes is as explained in the text.
Fig 4.
Functional brain network from EEG signals.
(left) An example of network mapping the functional brain connections recorded by EEG signals on the speaker’s scalp (labels). The applied threshold w0 = 0.06. The colour of nodes indicates two identified communities—frontal (F) and parietal (P). (right) Loss of the F/P community structure at a lower threshold w0 = 0.05 in the correlation matrix.
Fig 5.
SBN for speakers and listeners in stimulus1.
In each network, nodes represent different scalp locations (labels) while each link indicates the positive correlation that exceeds the threshold among the corresponding pair of EEG signals. Remarkably, each connectivity network visually splits into two clusters, which can be identified by the location labels as frontal (upper) and parietal (lower), also confirmed by the community detection analysis. See an example of a larger picture in Fig 4.
Table 1.
Graph-theoretic characteristics of single-brain connectivity networks.
Standard graph measures denoted in the first column (see Methods) for each SBN of the two speakers and two groups of listeners listed in the top row; data are for the case of stimulus1. For comparison, we also show the number of topology levels qmax and the number of cliques of the highest order in the corresponding network.
Fig 6.
The similarity of single-brain networks in stimulus1.
SBN overlaps between each listener in group 1 with speakers S1 and S2, the left panel, and each listener in group 2 with S1 and S2, right panel. Bottom left and right panels show the SBN overlaps between pairs of listeners in the group 1 and group 2, respectively. For a comparison, the SBN overlap between two speakers is also shown (the first bar in each bottom panel). Note that all overlaps are significantly larger than in the corresponding randomised models (see Figs C and D in S1 File).
Fig 7.
The similarity of single-brain networks in stimulus11.
Link overlaps of the two listener’s groups, explanations as for Fig 6, but with the speakers S1 and S3, respectively.
Fig 8.
Graph edit distances between listeners and speakers.
In stimulus1 (left) and stimulus11 (right), the speaker S1 is placed in the origin and the speaker S2 (S3) at the corresponding distance along the x-axis while the coordinates of the listeners of both groups are shown in the distance plane. Concerning GED, both groups of listeners systematically appear closer to a “right” speaker (S2 in stimulus1, S1 in stimulus11) according to the listeners’ subjective ratings.
Fig 9.
Topology measures of the SBNs of speakers and two groups of listeners for stimulus1.
Components of the second (SSV) and third (TSV) structure vectors (left panels), and the ranking distributions of the nodes’ topological dimensions (right panels). The full lines indicate the corresponding measures of the speakers.
Fig 10.
Structure of inter-brain linking.
Superbrain network of the speaker S2 and the listener L2−3, illustrating proper coordination, (left), and a weakly connected two-brain structure of the listener L1−4 and the speaker S1, corresponding to wrong coordination (right). Different colour of nodes indicates the identified functional communities. The node’s labels belong to the unique list of 882 scalp locations of all participants; for example, L2−3−TP9 and S2−F7 indicate the channel “TP9” on the scalp of the listener 3 in group 2, and channel “F7” of the scalp of the speaker S2, respectively. See also the overlaps in Fig 6 and distances in Fig 8 for these pairs.
Fig 11.
Communities, marked by different color, of nodes in the whole multi-brain network in stimulus1 (a), and stimulus11 (b). The nodes’ labels comprise the unique list of 882 scalp locations of all participants, as explained in the caption to Fig 10.
Fig 12.
Speaker-related communities occurring in multi-brain network.
Two communities dominated by frontal and parietal lobe locations are shown as separate graphs in stimulus1, (a) and (b), and in stimulus11 (c), and (d).
Fig 13.
Topology vectors of multi-brain graphs.
Left panels: Components of the first (FSV) and the third (TSV) structure vectors plotted against the topology level q for the whole multi-brain network and for some its subgraphs, as indicated in the corresponding legends. The additive components of the FSV allow a comparison of the whole MBN with the sum of the corresponding component of each participating SBN, the line is indicated by , where k runs over all listeners and the two speakers in stimulus1. TSV of cross-graphs in two-brain networks from Fig 10 and their counterparts are shown. Right panels: Components of the second (SSV) structure vector of the largest four communities in stimulus11 (top) and three communities in stimulus1 (bottom). For comparison, the values obtained for the corresponding SBN of the speakers and listeners participating in these communities are also shown.
Fig 14.
Subject-specific brain activity patterns of the speaker S1.
From left to right, SBNs represent EEG correlation patterns of the speaker S1 narrating a fairy tale (in stimulus1) and giving instructions (in stimulus11), and the components of the first and the second topology vector of these SBNs.
Fig 15.
Evolution of the brain-to-brain distance.
The timeline (16 frames) of the GED between brain activity networks of the listeners in group 1 (left) and in group 2 (right) from both speakers S1 and S2 is shown for the stimulus1. Each circle indicates the minimum distance from the corresponding speaker that occurred during the process.