EEG hyperscanning during multiuser gaming offers opportunities to study brain characteristics of social interaction under various paradigms. In this study, we aimed to characterize neural signatures and phase-based functional connectivity patterns of gaming strategies during collaborative and competitive alpha neurofeedback games. Twenty pairs of participants with no close relationship took part in three sessions of collaborative or competitive multiuser neurofeedback (NF), with identical graphical user interface, using Relative Alpha (RA) power as a control signal. Collaborating dyads had to keep their RA within 5% of each other for the team to be awarded a point, while members of competitive dyads scored points if their RA was 10% above their opponent's. Interbrain synchrony existed only during gaming but not during baseline in either collaborative or competitive gaming. Spectral analysis and interbrain connectivity showed that in collaborative gaming, players with higher resting state alpha content were more active in regulating their RA to match those of their partner. Moreover, interconnectivity was the strongest between homologous brain structures of the dyad in theta and alpha bands, indicating a similar degree of planning and social exchange. Competitive gaming emphasized the difference between participants who were able to relax and, in this way, maintain RA, and those who had an unsuccessful approach. Analysis of interbrain connections shows engagement of frontal areas in losers, but not in winners, indicating the formers' attempt to mentalise and apply strategies that might be suitable for conventional gaming, but inappropriate for the alpha neurofeedback-based game. We show that in gaming based on multiplayer non-verbalized NF, the winning strategy is dependent on the rules of the game and on the behavior of the opponent. Mental strategies that characterize successful gaming in the physical world might not be adequate for NF-based gaming.

Objective: to characterize the direction within and between brain connectivity in winning and losing players in a competitive brain-computer interface game. Approach: ten dyads (26.9 ± 4.7 years old, eight females and 12 males) participated in the study. In a competitive game based on neurofeedback, they used their relative alpha (RA) band power from the electrode location Pz, to control a virtual seesaw. The players in each pair were separated into winners (W) and losers (L) based on their scores. Intrabrain connectivity was analyzed using multivariate Granger Causality (GC) and Directed Transfer Function, while interbrain connectivity was analyzed using bivariate GC. Results: linear regression analysis revealed a significant relationship (p<0.05) between RA and individual scores. During the game, W players maintained a higher RA than L players, although it was not higher than their baseline RA. The analysis of intrabrain GC indicated that both groups engaged in general social interactions, but only the W group succeeded in controlling their brain activity at Pz. Group L applied an inappropriate metal strategy, characterized by strong activity in the left frontal cortex, indicative of collaborative gaming. Interbrain GC showed a larger flow of information from the L to the W group, suggesting a higher capability of the W group to monitor the activity of their opponent. Significance: both innate neurological indices and gaming mental strategies contribute to game outcomes. Future studies should investigate whether there is a causal relationship between these two factors.

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B-L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits.

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Electroencephalography (EEG) has been widely used in clinical practice as a tool to guide an efficient post-stroke functional recovery program and adjust the rehabilitation training program. EEG has some advantages in temporal resolution, portability, and relatively low cost. Motor-related brain activities can be quantified by measuring the mu rhythm’s event-related desynchronization (ERD), acquired from a direct surface EEG recording. The common problem of direct surface EEG is the crosstalk between brain regions that are unrelated to the sensorimotor cortex. This problem can be solved through the EEG source localization technique. Essentially, the idea of source reconstruction is to estimate the primary cortical current densities during the given EEG recording across the head surface. The recent work aims to compare EEG power spectral density (PSD) analysis and source localization. Modification of exact-Low Resolution Brain Electromagnetic Tomography (eLORETA) was used as an EEG source localization method. EEG data recording was performed on a healthy, able-bodied male subject using 21 electrodes based on the case study’s 10-20 electrode placement system. The subject was asked to perform basic hand movements, i.e., grasping with and without hand load. Mu power was calculated from the electrodes that are located around the motor cortex (C3 and C4), while the average current density was calculated from the Brodmann Area (BA) 4 and 6, both during grasping and relaxation (baseline). The comparison showed that during grasping, there was a decrease in aver-age current density around BA-4 and BA-6 and a decrease in mu power at C3 and C4 when compared to baseline. These results indicate: (1) the activity of mu rhythm’s ERD around the sensorimotor cortex during hand movement; and (2) the possibility of using the EEG source localization method as an alternative biofeedback modality.