Home › Cell Biology › A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
Cell Biology JoVE (Open Access) Citable · DOI

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

DOI: 10.3791/53406-v
What you'll learn
  • ✓ Set up high-density EEG and inertial measurement units for infant neural recording
  • ✓ Collect synchronized neural and kinematic data during natural social interaction
  • ✓ Apply source imaging and decoding algorithms to infer behavioral intent from EEG
  • ✓ Interpret multimodal neural signals predicting infant motor actions
Protocol

This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.

Difficulty
advanced
Total time
~2–3 hours per infant session (including preparation, recording, and post-processing setup)

Steps

1
Prepare high-density EEG and IMU systems

Configure active scalp electroencephalography electrode arrays and inertial measurement units for signal acquisition. Ensure all sensors are calibrated and properly interfaced with data acquisition hardware.

▶ 02:30
2
Record neural and kinematic data during interaction

Acquire synchronized EEG and IMU data while infant engages in unscripted social interaction with an actor. Simultaneously capture video for behavioral reference and annotation.

▶ 05:12
3
Post-process data and perform source imaging

Clean and preprocess EEG signals, apply source localization techniques, and register neural activity to anatomical brain space. Align kinematic and video data with neural timestamps.

▶ 07:52
4
Decode intent from multimodal neural signals

Apply machine learning or decoding models to predict behavioral actions and intent from EEG activity. Validate predictions against observed kinematic and behavioral outcomes.

▶ 09:08
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