Control device and method for EEG-based wearable robots

EEG-based wearable robot control technology with head motion artifact removal

This technology is an EEG-based wearable robot control device and method that filters out highly correlated components by linking head motion sensor data with independent component analysis (ICA) of EEG signals to remove artifacts caused by head movement, thereby generating training data for classifying gait intention based on pure EEG signals.

Conventional technologies rely on multiple sensors, such as foot pressure sensors, to determine gait intention, which compromises durability. Furthermore, they face technical limitations in accurately reflecting user intent due to noise interference caused by head movement during EEG measurement.

This technology proposes a method of collecting EEG signals per movement unit using head motion sensors and EEG detectors, identifying and removing components highly correlated with head movement through independent component analysis, and then extracting features from the remaining signals. It can be applied to lower-limb rehabilitation and gait assistance robots, accurately detecting a user's gait intention using only EEG signals without the need for additional sensors.

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Key Features:

• Receiving information on the user's head movement and the wearable robot induced by the user
• Collecting the user's EEG signals detected during movement and storing them by movement unit
• Separating the collected EEG signals into multiple independent components through independent component analysis
• Generating training data by removing independent components that have a correlation with head movement information above a threshold value

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This invention was developed with the support of the Ministry of Science, ICT and Future Planning for the development of vehicle driving and hazard recognition technology through automated brain signal analysis.