Summary
Every year, half a million people become paralysed by a spinal cord injury. Assistive technologies, such as Brain-Computer Interfaces (BCIs), can improve their mobility, independence, and overall well-being. BCIs bypass a neurological injury by reading the user’s intent from their brain activity and using it to control a computer cursor or a robotic arm, or even to reanimate their own paralysed limbs. Despite these remarkable feats, BCIs still face challenges that prevent their widespread use.
Here, I propose to address two of their main shortcomings: their unintuitive control that produces unskilled movements, and their instable performance that decays over time. The brain controls movement by coordinating the activity of many areas. Thus, unintuitive BCI control might be due to their reliance on the activity of a single cortical region (typically motor cortex) to decode the user’s intent. I will develop a new type of BCI that reads the activity of the striatum, a subcortical area that receives inputs from the entire cortex and has been shown to be critical for skilled movements. My hypothesis is that a BCI based on striatal activity will mimic the execution of a skilled movement. Using large-scale neural recording techniques and emerging computational techniques, I will identify striatal population dynamics and use them as input for the BCI, expecting to show that such an approach outperforms current BCIs. Next, I will address the instability problem. BCI instability is mainly caused by the inevitable changes in recorded neurons over long timescales. My host has recently developed a method that reveals the ‘true’ cortical dynamics underlying a given behaviour. I will adopt this method for the proposed BCI to stabilise its performance over long time periods.
If successful, this project will lead to BCIs that are easier to use, more precise, and stable. Their future translation to humans could bring BCIs closer to the clinic, with considerable socioeconomic impact.
Here, I propose to address two of their main shortcomings: their unintuitive control that produces unskilled movements, and their instable performance that decays over time. The brain controls movement by coordinating the activity of many areas. Thus, unintuitive BCI control might be due to their reliance on the activity of a single cortical region (typically motor cortex) to decode the user’s intent. I will develop a new type of BCI that reads the activity of the striatum, a subcortical area that receives inputs from the entire cortex and has been shown to be critical for skilled movements. My hypothesis is that a BCI based on striatal activity will mimic the execution of a skilled movement. Using large-scale neural recording techniques and emerging computational techniques, I will identify striatal population dynamics and use them as input for the BCI, expecting to show that such an approach outperforms current BCIs. Next, I will address the instability problem. BCI instability is mainly caused by the inevitable changes in recorded neurons over long timescales. My host has recently developed a method that reveals the ‘true’ cortical dynamics underlying a given behaviour. I will adopt this method for the proposed BCI to stabilise its performance over long time periods.
If successful, this project will lead to BCIs that are easier to use, more precise, and stable. Their future translation to humans could bring BCIs closer to the clinic, with considerable socioeconomic impact.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101025630 |
| Start date: | 01-06-2021 |
| End date: | 30-09-2023 |
| Total budget - Public funding: | 212 933,76 Euro - 212 933,00 Euro |
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Original description
Every year, half a million people become paralysed by a spinal cord injury. Assistive technologies, such as Brain-Computer Interfaces (BCIs), can improve their mobility, independence, and overall well-being. BCIs bypass a neurological injury by reading the user’s intent from their brain activity and using it to control a computer cursor or a robotic arm, or even to reanimate their own paralysed limbs. Despite these remarkable feats, BCIs still face challenges that prevent their widespread use.Here, I propose to address two of their main shortcomings: their unintuitive control that produces unskilled movements, and their instable performance that decays over time. The brain controls movement by coordinating the activity of many areas. Thus, unintuitive BCI control might be due to their reliance on the activity of a single cortical region (typically motor cortex) to decode the user’s intent. I will develop a new type of BCI that reads the activity of the striatum, a subcortical area that receives inputs from the entire cortex and has been shown to be critical for skilled movements. My hypothesis is that a BCI based on striatal activity will mimic the execution of a skilled movement. Using large-scale neural recording techniques and emerging computational techniques, I will identify striatal population dynamics and use them as input for the BCI, expecting to show that such an approach outperforms current BCIs. Next, I will address the instability problem. BCI instability is mainly caused by the inevitable changes in recorded neurons over long timescales. My host has recently developed a method that reveals the ‘true’ cortical dynamics underlying a given behaviour. I will adopt this method for the proposed BCI to stabilise its performance over long time periods.
If successful, this project will lead to BCIs that are easier to use, more precise, and stable. Their future translation to humans could bring BCIs closer to the clinic, with considerable socioeconomic impact.
Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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