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Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision
Mauricio Adolfo Ramírez-Moreno1, David Gutiérrez1
1Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey, Apodaca, Nuevo Leon 66600, Mexico.
Computational Intelligence and Neuroscience
|December 31, 2019
Summary
This study introduces a semiautonomous brain-computer interface (BCI) for robotic arm control. The new goal-selection approach significantly improves pick-and-place task performance and reduces user mental fatigue compared to traditional methods.
Area of Science:
- Robotics
- Neuroscience
- Human-Computer Interaction
Background:
- Traditional brain-computer interfaces (BCI) require continuous user input for robotic control, leading to fatigue in repetitive tasks.
- Semiautonomous systems offer a potential solution to reduce user workload in complex manipulation tasks.
Purpose of the Study:
- To evaluate a semiautonomous BCI system for robotic arm manipulation tasks.
- To compare the performance and mental fatigue associated with a novel goal-selection BCI approach versus traditional process-control BCI.
Main Methods:
- Developed a semiautonomous BCI using a conformal geometric algebra model for real-time inverse kinematics.
- Integrated an artificial vision algorithm for object localization and goal-selection by the user.
- Implemented pick-and-place tasks with human participants comparing two BCI control schemes.
Main Results:
- The semiautonomous goal-selection BCI demonstrated superior performance in pick-and-place tasks.
- Users reported significantly less mental fatigue when using the semiautonomous approach compared to the continuous control method.
Conclusions:
- Semiautonomous BCI systems, particularly the goal-selection approach, offer a more efficient and less demanding method for robotic manipulation.
- This BCI strategy enhances user experience and task efficiency in robotic control applications.
