A non-invasive brain-computer interface captured scalp electrical signals and translated intended actions into device commands. An AI co-pilot inferred user goals and reduced the need to decode detailed brain activity. Four participants completed a cursor-targeting task most of the time, with faster completion and higher success rates when the AI co-pilot assisted. An AI-assisted BCI controlled a robotic arm to pick up and place coloured blocks; a man with partial paralysis could not complete the task with the conventional device but achieved 93% success with the AI co-pilot. AI collaboration improved speed and accuracy.
When the authors added an AI co-pilot to the device, the participants completed the task more quickly and had a higher success rate. The device with the co-pilot doesn't need to decode as much brain activity because the AI can infer what the user wants to do, says Kao. "These co-pilots are essentially collaborating with the BCI user and trying to infer the goals that the BCI user is wishing to achieve, and then helps to complete those actions," he adds.
The researchers also trained an AI co-pilot to control a robotic arm. The participants were required to use the robotic arm to pick up coloured blocks and move them to marked spots on a table. The person with paralysis could not complete the task using the conventional, non-invasive BCI, but was successful 93% of the time using the BCI with an AI co-pilot.
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