BCI Rehabilitation Robot Control for Real-Time Motor Intention
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Solution Overview
Problem
Existing rehabilitation treatments are passive and do not effectively reflect the patient's intention, leading to suboptimal motor function restoration.
Innovation Solution
A rehabilitation robot control apparatus utilizing a brain-computer interface (BCI) that measures and analyzes brainwave signals in real-time to classify the patient's motor intention and adjust the rehabilitation robot's operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If passive rehabilitation treatment is used, then the rehabilitation process is simple to implement, but the patient's motor function restoration effectiveness is reduced
Solution Approach 1:
The system continuously monitors the patient's brainwave signals and provides real-time feedback to adjust the rehabilitation robot's operations. The classification device analyzes motor intention from EEG signals and feeds this information back to the controller, which dynamically adjusts the robot's assistance level and exercise parameters, creating a closed-loop control system that adapts to the patient's changing needs.
Solution Approach 2:
The rehabilitation system enables the patient to actively control the rehabilitation process through their own motor intentions detected via brainwave signals. The patient self-regulates the intensity and type of rehabilitation exercises by concentrating on specific motor tasks, with the robot automatically adjusting its assistance based on the detected neural activity, reducing the need for constant therapist intervention.
2Measurement precision
If real-time brainwave analysis is implemented, then the patient's motor intention can be accurately reflected, but the system complexity increases
Solution Approach 1:
The preprocessing device serves as an intermediary between the brainwave signal measuring device and the classification device. It performs essential signal processing tasks including filtering to remove noise, artifact removal to eliminate non-neural signals, and feature extraction to identify relevant motor intention patterns, thereby simplifying the input to the classification algorithm and improving detection accuracy.
Solution Approach 2:
The system is divided into distinct functional modules: the brainwave signal measuring device (EEG electrodes), the preprocessing device (signal processing algorithms), the classification device (motor intention recognition), and the controller (robot control). This segmentation allows each component to be optimized independently and facilitates easier debugging, maintenance, and adjustment of the overall system.
3Productivity
If active rehabilitation treatment with real-time intention reflection is used, then the patient's participation and concentration increase, but the control system becomes more complex
Solution Approach 1:
The rehabilitation robot's control parameters are dynamically adjusted based on real-time brainwave analysis. The controller continuously modifies the level of robotic assistance, exercise intensity, and movement parameters according to the patient's current motor intention and engagement level, enabling the system to adapt to changing patient needs during the rehabilitation session without requiring manual reconfiguration.
Data Source
AI summary
An embodiment rehabilitation robot control apparatus includes a brainwave signal measuring device configured to measure a brainwave signal of a user, a preprocessing device configured to preprocess the measured brainwave signal, a classification device configured to classify a motor intention of the user based on the brainwave signal preprocessed by the preprocessing device, and a controller configured to reflect the motor intention of the user in real time to control an operation or a stop of a rehabilitation robot.