A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

Publications – Redwood Center for Theoretical Neuroscience

Neelesh Kumar

Leila Wehbe's Homepage

Deep Learning Methods for EEG Neural Classification

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Key Findings

From brain to movement: Wearables-based motion intention prediction across the human nervous system - ScienceDirect

Deep Learning Methods for EEG Neural Classification

Frontiers Experiment protocols for brain-body imaging of locomotion: A systematic review

PDF) Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

Neural representational geometry underlies few-shot concept learning

Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks - Gatti - 2021 - European Journal of Neuroscience - Wiley Online Library

From brain to movement: Wearables-based motion intention prediction across the human nervous system - ScienceDirect

Sensors, Free Full-Text