| Autor: |
Maksimenko VA; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Hramov AE; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Frolov NS; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Lüttjohann A; Institute of Physiology I, University of Münster, Münster, Germany., Nedaivozov VO; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Grubov VV; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Runnova AE; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Makarov VV; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia., Kurths J; Potsdam Institute for Climate Impact Research, Potsdam, Germany.; Department of Physics, Humboldt University, Berlin, Germany.; Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom., Pisarchik AN; REC 'Artificial Intelligence Systems and Neurotechnology', Yuri Gagarin State Technical University of Saratov, Saratov, Russia.; Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain. |
| Abstrakt: |
Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions. |
