Objective. The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain–machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements. Approach. Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection. Main results. A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved. Significance. The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user’s involvement during gaze-based interaction.

Voice- and gaze-based hands-free input are increasingly used in human-machine interaction. Attempts to combine them into a hybrid technology typically employ the voice channel as an information-rich channel. Voice seems to be “overqualified” to serve simply as a substitute of a computer mouse click, to confirm selections made by gaze. It could be expected that the user would feel discomfort if they had to frequently make “clicks” using their voice, or easily get bored, which also could lead to low performance. To test this, we asked 23 healthy participants to select moving objects with smooth pursuit eye movements. Manual confirmation of selection was faster and rated as more convenient than voice-based confirmation. However, the difference was not high, especially when voice was used to pronounce objects’ numbers (speech recognition was not applied): Score of convenience (M ± SD) was 9.2 ± 1.1 for manual and 8.0 ± 2.1 for voice confirmation, and time spent per object was 1269 ± 265 ms and 1626 ± 331 ms, respectively. We conclude that “voice-as-click” can be used to confirm selection in gaze-based interaction with computers as a substitute for the computer mouse click when manual confirmation cannot be used.

In the present study, we combine linguistic annotation of oral texts in Russian with the registration of BOLD signal in functional MRI experiments to determine how and where semantic categories are represented in the human brain. Using the same stimuli material, we also analyze the differences in cortical activation in three thematic domains: description of nature, description of working principles of technical devices and more self-referential texts, addressing the question of human identity in conflict situations. We discuss methodological problems within the two approaches (microanalysis and macroanalysis) to study brain activation in natural conditions, i.e. under a continuous speech flow. Within the thematic domain studies, only minimally significant differences in brain activation were registered during the listening to texts from the three thematic groups. This outcome leads to the conclusion that the approach of thematic group contrasts (cognitive subtraction methodology) is not sufficient to study the mechanisms of text comprehension, and should be replaced by the modeling of multidimensional representations of semantic categories in time. Within the semantic category approach, we describe the neurolinguistic process of text understanding as the activation of 15 clusters responsible for semantic categories (e.g. "Conflict", "Mental", "Social"). Our data demonstrate that the clusters are widely distributed across the human brain. In contrast to the previous studies, we suggest that deep subcortical structures are involved in the processing of certain categories as well. The observed lateralization of category processing underlines the involvement of the right hemisphere in the processing of meaning.

The performance of the human brain depends on how effectively its distinct regions communicate, especially the regions which are more strongly connected to each other than to other regions, or so called “rich-clubs”. The aim of the current work is to find a connectivity pattern between the three brain rich-club regions without any a priori assumptions on the underlying network architecture. Rich-clubs for the analysis were previously identified with structural MRI. Functional magnetic resonance imaging (fMRI) data from 25 healthy subjects (1000 time points from each one) was acquired and Transfer Entropy (TE) between fMRI time-series from rich-clubs was calculated. The significant results at the group level were obtained by testing against the surrogate data generated on a novel approach. We found stable causal interactions between rostral Anterior Cingulate Cortex L and Dorsal Anterior Cingulate Cortex L, dorsal Anterior Cingulate Cortex L and Paracentral Lobule R but not vice versa. Our work provides an approach to causal analysis of experimental data and demonstrates the applicability to real fMRI study.

Insight is described as the sudden solution of a problem and is contrasted with an analytical, step-by-step approach. Traditionally, insight is thought to be associated with activity of the right hemisphere, whereas analytical solutions are thought to be associated with activity of the left hemisphere. However, empirical evidence as to the localization of insight-related brain activity is mixed and inconclusive. Some studies seem to confirm the traditional view, whereas others do not. Moreover, results of EEG and fMRI studies frequently contradict each other. In this study, EEG and fMRI data were recorded while subjects performed the remote association test and for each solved problem were asked to report whether the solution was reached analytically or insightfully. The data were analyzed in a 16-second fragment preceding the subject’s response. Source localization techniques were used in the analysis of EEG data. Based on EEG data, insightful as compared to analytical problem solving was accompanied by high-frequency synchronization in semantic cortical areas of the left hemisphere 10–12 s before the subject’s response. Based on fMRI data, however, insightful solutions were accompanied by increased activity in frontal and temporal regions of the right hemisphere. The results are interpreted in terms of different cognitive processes involved in insightful problem solving, which could be differently reflected in EEG and fMRI data.