Summary
More than 25% of people over the age of 50 suffer a movement disorder; many of them have a functional disability as a result.
Intracortical Brain-Computer Interfaces (BCIs) that extract or “decode” the user’s intent based on recordings from tens or hundreds of cortical neurons have enabled paralyzed patients to control computer cursors, robots, or even their own limbs just by modulating their neural activity. Despite these successes, BCI control still falls short compared to natural limb control: it is far less skilled, and it requires significantly more mental effort.
My goal is to overcome these two limitations through a novel “Intuitive BCI” that leads to enhanced control compared to current approaches. Rather than follow the dominant trends of developing new decoder algorithms and interface technologies, I will adopt a unique, previously unexplored approach: to leverage the brain’s own distributed resources to control automatic movements in a skilled and intuitive manner requiring little mental effort.
Automatic movements depend critically on subcortical brain structures whose activity is not probed by current cortical BCIs. To develop my novel approach to BCI based on the neural control of automatic movements, I will combine: 1) recent breakthroughs to record from thousands of neurons across the mouse brain; 2) a novel theory to model neural population activity that I have helped establish; and 3) new experimental tasks to isolate the cortico-subcortical information underlying automatic movements. This information will provide inputs to the proposed Intuitive BCI. I seek to prove that it leads to more skilled and intuitive control than current approaches, even allowing users to multi-task –an unprecedented feat in the field.
This timely, multidisciplinary research will push BCI control forward, beyond current state-of-the-art approaches limited to cortical control. It will also help realize BCI’s promise to improve the quality of life of neurological patients
Intracortical Brain-Computer Interfaces (BCIs) that extract or “decode” the user’s intent based on recordings from tens or hundreds of cortical neurons have enabled paralyzed patients to control computer cursors, robots, or even their own limbs just by modulating their neural activity. Despite these successes, BCI control still falls short compared to natural limb control: it is far less skilled, and it requires significantly more mental effort.
My goal is to overcome these two limitations through a novel “Intuitive BCI” that leads to enhanced control compared to current approaches. Rather than follow the dominant trends of developing new decoder algorithms and interface technologies, I will adopt a unique, previously unexplored approach: to leverage the brain’s own distributed resources to control automatic movements in a skilled and intuitive manner requiring little mental effort.
Automatic movements depend critically on subcortical brain structures whose activity is not probed by current cortical BCIs. To develop my novel approach to BCI based on the neural control of automatic movements, I will combine: 1) recent breakthroughs to record from thousands of neurons across the mouse brain; 2) a novel theory to model neural population activity that I have helped establish; and 3) new experimental tasks to isolate the cortico-subcortical information underlying automatic movements. This information will provide inputs to the proposed Intuitive BCI. I seek to prove that it leads to more skilled and intuitive control than current approaches, even allowing users to multi-task –an unprecedented feat in the field.
This timely, multidisciplinary research will push BCI control forward, beyond current state-of-the-art approaches limited to cortical control. It will also help realize BCI’s promise to improve the quality of life of neurological patients
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/949660 |
| Start date: | 01-12-2021 |
| End date: | 30-11-2026 |
| Total budget - Public funding: | 1 500 000,00 Euro - 1 500 000,00 Euro |
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Original description
More than 25% of people over the age of 50 suffer a movement disorder; many of them have a functional disability as a result.Intracortical Brain-Computer Interfaces (BCIs) that extract or “decode” the user’s intent based on recordings from tens or hundreds of cortical neurons have enabled paralyzed patients to control computer cursors, robots, or even their own limbs just by modulating their neural activity. Despite these successes, BCI control still falls short compared to natural limb control: it is far less skilled, and it requires significantly more mental effort.
My goal is to overcome these two limitations through a novel “Intuitive BCI” that leads to enhanced control compared to current approaches. Rather than follow the dominant trends of developing new decoder algorithms and interface technologies, I will adopt a unique, previously unexplored approach: to leverage the brain’s own distributed resources to control automatic movements in a skilled and intuitive manner requiring little mental effort.
Automatic movements depend critically on subcortical brain structures whose activity is not probed by current cortical BCIs. To develop my novel approach to BCI based on the neural control of automatic movements, I will combine: 1) recent breakthroughs to record from thousands of neurons across the mouse brain; 2) a novel theory to model neural population activity that I have helped establish; and 3) new experimental tasks to isolate the cortico-subcortical information underlying automatic movements. This information will provide inputs to the proposed Intuitive BCI. I seek to prove that it leads to more skilled and intuitive control than current approaches, even allowing users to multi-task –an unprecedented feat in the field.
This timely, multidisciplinary research will push BCI control forward, beyond current state-of-the-art approaches limited to cortical control. It will also help realize BCI’s promise to improve the quality of life of neurological patients
Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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