Disentangling human grasping type from the object's intrinsic properties using low-frequency EEG signals.

Area of Science:

• Neuroscience
• Motor Control
• Brain-Computer Interfaces

Background:

• Fronto-parietal brain networks are activated during grasping in humans and primates.
• The neural representation of object properties versus hand postures in grasping remains unclear.

Purpose of the Study:

• To investigate the spatiotemporal encoding of object properties and grasp types in human electroencephalography (EEG) during grasping.
• To develop and evaluate multiclass decoders for grasp type and object properties from EEG signals.
• To compare EEG representations with categorical models of movement and object properties.

Main Methods:

• Manipulated object properties and grasp types to create 12 unique grasping movements.
• Recorded low-frequency time-domain EEG signals from healthy adults during grasping.
• Implemented and evaluated multiclass decoders for grasp type and object properties over time.
• Analyzed the similarity between EEG representations and categorical models.

Main Results:

• Object properties (shape, size) and grasp types (movement properties) are encoded in distinct brain areas throughout grasping stages.
• Both grasp types and object properties were significantly decodable from EEG during planning and execution.
• Object properties were decodable from the observation stage, while grasp types were decodable at object release.

Conclusions:

• Noninvasive EEG signals can differentiate the neural encoding of object intrinsic properties and movement parameters during grasping.
• The temporal evolution of neural representations allows for decoding of both object and action information at different movement stages.
• Developed decoders offer insights for noninvasive motor control strategies and brain-computer interfaces.