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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Increasing Human Performance by Sharing Cognitive Load Using Brain-to-Brain Interface

Texto completo
Maksimenko, Vladimir A. [1] ; Hramov, Alexander E. [1] ; Frolov, Nikita S. [1] ; Luettjohann, Annika [2] ; Nedaivozov, Vladimir O. [1] ; Grubov, Vadim V. [1] ; Runnova, Anastasia E. [1] ; Makarov, Vladimir V. [1] ; Kurths, Juergen [3, 4, 5] ; Pisarchik, Alexander N. [1, 6]
Número total de Autores: 10
Afiliação do(s) autor(es):
[1] Yuri Gagarin State Tech Univ Saratov, REC Artificial Intelligence Syst & Neurotechnol, Saratov - Russia
[2] Univ Munster, Inst Physiol 1, Munster - Germany
[3] Humboldt Univ, Dept Phys, Berlin - Germany
[4] Potsdam Inst Climate Impact Res, Potsdam - Germany
[5] Univ Aberdeen, Inst Complex Syst & Math Biol, Aberdeen - Scotland
[6] Tech Univ Madrid, Ctr Biomed Technol, Madrid - Spain
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Citações Web of Science: 9

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. (AU)

Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
Beneficiário:Elbert Einstein Nehrer Macau
Linha de fomento: Auxílio à Pesquisa - Temático