Multimodal BCI-mediated FES suppression of pathological tremor

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Robotics

Background:

• Tremor is a common movement disorder affecting 14.5% of adults aged 50-89, causing significant disability in daily activities for many.
• A substantial portion of patients (25%) show resistance to conventional treatments like medication or neurosurgery.
• The TREMOR project aims to address these limitations by developing a novel assistive system.

Purpose of the Study:

• To develop a multimodal Brain Neural Computer Interface (BNCI) system for assessing volitional and tremorous upper limb movements.
• To integrate electroencephalography (EEG), electromyography (EMG), and inertial measurement units (IMUs) for comprehensive movement analysis.
• To generate control signals for a soft wearable robot utilizing functional electrical stimulation (FES).

Main Methods:

• A multimodal BNCI was designed, integrating EEG for volitional intent detection, EMG for tremor onset and frequency analysis, and IMUs for kinematic tremor data.
• The system processes EEG signals to identify the intention to move.
• EMG and IMU data are analyzed to characterize tremor dynamics, including amplitude and frequency.

Main Results:

• The multimodal BCI successfully integrates EEG, EMG, and IMU data to provide a comprehensive assessment of movement and tremor.
• The system can detect volitional movement intention from cortical activity (EEG).
• Tremor characteristics such as onset, frequency, amplitude, and instantaneous frequency are accurately estimated using EMG and IMU signals.

Conclusions:

• The developed multimodal BCI system provides essential data for controlling assistive devices.
• Integration of EEG, EMG, and IMU signals enables precise functional compensation for upper limb tremors.
• This approach offers a promising pathway for developing effective wearable robots for tremor management.