Evaluating corticokinematic coherence using electroencephalography and human pose estimation.

Area of Science:

• Neuroscience
• Biomechanics
• Human-Computer Interaction

Background:

• Proprioception's peripheral mechanisms are understood, but cortical processing during complex movements is unclear.
• Corticokinematic coherence (CKC) measures sensorimotor cortex and limb movement coupling, but assessment is challenging.
• Ecologically valid CKC measurement requires advanced, integrated methodologies.

Purpose of the Study:

• To validate a novel methodology integrating Electroencephalography (EEG) and Human Pose Estimation (HPE) for measuring CKC.
• To assess the feasibility and validity of this EEG-HPE approach in real-world and virtual reality (VR) settings.
• To enable more accessible assessment of cortical proprioceptive processing.

Main Methods:

• Nine healthy adults performed finger-tapping and reaching tasks in real and VR environments.
• Data acquisition involved synchronized 64-channel EEG, optical motion capture, and deep-learning-based HPE (Mediapipe).
• CKC and kinematic agreement between marker-based and HPE systems were analyzed.

Main Results:

• CKC was successfully detected using both marker-based and HPE kinematics across tasks and environments.
• HPE-derived CKC closely matched marker-based measurements for most joints.
• Strong reliability and equivalent coherence magnitudes were found between real and VR conditions.

Conclusions:

• The study validates a noninvasive, portable EEG-HPE approach for assessing cortical proprioceptive processing.
• This method allows for ecologically valid CKC measurements.
• The approach has potential for broader clinical and rehabilitation applications.