A brain machine interface framework for exploring proactive control of smart environments

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system t...

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Bibliographic Details
Published in:Scientific reports 2024-05, Vol.14 (1), p.11054-17, Article 11054
Main Authors: Braun, Jan-Matthias, Fauth, Michael, Berger, Michael, Huang, Nan-Sheng, Simeoni, Ezequiel, Gaeta, Eugenio, Rodrigues do Carmo, Ricardo, García-Betances, Rebeca I., Arredondo Waldmeyer, María Teresa, Gail, Alexander, Larsen, Jørgen C., Manoonpong, Poramate, Tetzlaff, Christian, Wörgötter, Florentin
Format: Article
Language:English
Subjects:
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Algorithms
Animals
Brain - physiology
Brain research
Brain-Computer Interfaces
Calibration
Electrodes
Electroencephalography
Energy efficiency
Humanities and Social Sciences
Interfaces
Macaca mulatta
Monkeys & apes
multidisciplinary
Neurosciences
Physiology
Planning
Quality of life
Science
Science (multidisciplinary)
Smart devices
Smart houses
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Summary:Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-60280-7