TY - JOUR
T1 - A brain machine interface framework for exploring proactive control of smart environments
AU - Braun, Jan Matthias
AU - Fauth, Michael
AU - Berger, Michael
AU - Huang, Nan Sheng
AU - Simeoni, Ezequiel
AU - Gaeta, Eugenio
AU - Rodrigues do Carmo, Ricardo
AU - García-Betances, Rebeca I.
AU - Arredondo Waldmeyer, María Teresa
AU - Gail, Alexander
AU - Larsen, Jørgen C.
AU - Manoonpong, Poramate
AU - Tetzlaff, Christian
AU - Wörgötter, Florentin
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
U2 - 10.1038/s41598-024-60280-7
DO - 10.1038/s41598-024-60280-7
M3 - Journal article
C2 - 38744976
AN - SCOPUS:85193205456
SN - 2045-2322
VL - 14
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 11054
ER -