Wearable robot control device and method based on movement intention detection using brain-computer interface

Brain-Machine Interface Wearable Robot Control Technology Combining Tactile Stimulation Induction and Motion Artifact Removal

This technology collects brain signals generated by applying distinct tactile stimuli to both of the user's feet to induce motor imagery. It removes motion artifacts generated during robot movement using reference signals and independent component analysis, then calculates walking intent, speed, and stride length through a brain signal classifier to control the wearable robot.

Existing brain-machine interface technologies face challenges in intuitively distinguishing between right and left foot movement intentions during rehabilitation for patients with lower-limb paralysis, and the motion artifacts generated during robot operation distort brain signals, leading to lower accuracy in intent recognition.

This technology proposes a method that induces EEG patterns by applying tactile stimuli of different frequencies to the left and right feet, removes motion artifacts in real-time by using the robot's inertial sensor values as reference signals, and distinguishes walking intent through a multi-classifier ensemble. It can be used for the rehabilitation and gait reconstruction of patients with lower-limb paralysis, enabling intuitive robot control that accurately reflects the user's intent.

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Key Features:

• A step of presenting different tactile stimuli to the left and right feet of a user wearing a wearable robot
• A step of acquiring brain signals manifested by motor imagery and tactile stimulation, as well as reference signals for movement
• A step of determining the movement intent corresponding to the brain signals based on the brain signals and a brain signal classifier
• A step of transmitting the classifier's output and movement intent to a control command receiver to control the wearable robot

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This invention was developed with support from the Ministry of Science and ICT under the project "Development of Non-invasive BCI Integrated Brain-Cognitive Computing SW Platform Technology for Controlling Real-life Devices and AR/VR Devices with Thoughts" (BCI-General/Sub-project 1) and "Development of BCI-based Brain-Cognitive Computing Technology for Recognizing Human Intent Using Deep Learning" (BCI-Sub-project 2).