| Autor: |
Jan-Matthias Braun, Michael Fauth, Michael Berger, Nan-Sheng Huang, Ezequiel Simeoni, Eugenio Gaeta, Ricardo Rodrigues do Carmo, Rebeca I. García-Betances, María Teresa Arredondo Waldmeyer, Alexander Gail, Jørgen C. Larsen, Poramate Manoonpong, Christian Tetzlaff, Florentin Wörgötter |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-60280-7 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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