ArticleScientific reports2024
A brain machine interface framework for exploring proactive control of smart environments.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Encoding of blink information via wireless contact lens for eye-machine interaction.National science review · 2025Article
- Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.APL bioengineering · 2025Article
- Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex.Nature communications · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
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.
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Registered trials
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