Algorithm Development for Restoring Movement After Paralysis
This study is developing new algorithms to help people with paralysis regain complex movement. You would participate in experiments where non-invasive sensors (like EEG, which measures brain waves, or EMG, which measures muscle activity) record your activity. You might be asked to imagine, try, or perform actions to control a computer, robotic arm, or wheelchair. The goal is to see how well these recorded signals can be used to guide these devices. We are looking for 50 healthy English-speaking adults aged 18 and older who do not have neurological injuries or diseases causing paralysis. Success will be measured by how well the algorithms perform.
- Study design
- This interventional study plans to enroll 50 healthy participants. It is not specified if it is randomized or blinded.
- What's involved
- You would typically have one study visit, with possible additional visits within a month. During these visits, you would wear sensors and perform, imagine, or attempt movements or speech.
- Compensation
- Not stated in the trial record.
- Follow-up
- Not specified.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Towards Restoring Complex Movement After Paralysis: Algorithm Development With Healthy Participants
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Jonathan Kao, PhD · PRINCIPAL_INVESTIGATOR · UCLA Neural Engineering and Computation Lab
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Normalized PerformanceUsually one visit (Day 1), with possible additional study visits usually within a month.
The normalized performance of the non-invasive assistive interface on a task. Non-invasive signals, which may include electroencephalography, electromyography, functional near infrared spectroscopy, inertial measurements units, eye movements, pupil size, and speech are input into an algorithm that controls an end effector's movements. The end effector, which may be a computer cursor, robotic manipulator, wheelchair, or other assistive device, is used to perform a motor task. The normalized performance is derived from the the performance of the end effector on the motor task, reflecting the overall performance of the non-invasive assistive interface. The minimum value is zero. There is no maximum value, although the values are usually less than 1. Higher is better.