IoT-V2E

PyTorch implementation on: IoT-V2E: an uncertainty-aware cross-modal hashing retrieval between infrared-videos and EEGs for automated sleep state analysi

Introduction

we propose a novel IoT system (IoT-V2E) for monitoring and analysing sleep state at home using ubiquitous IR visual camera sensors.

Specifically, IoT-V2E is a uncertainty-aware cross-modal hashing retrieval system that finds the most similar EEG signal representation in a database, rather than using a sleep stage classification paradigm, for sleep stage inference and analysis.

Getting Started

Requirmenets:

python >= 3.6.10
pytorch >= 1.1.0
FFmpeg, FFprobe
Numpy
Sklearn
Pandas
openpyxl
mne=='0.20.7'

Train:：

Run trainmm.py (single-GPU training)

You need to input some parameters based on your own settings, including hash code length, the location of the cross-modal dataset, and the location of generated hash codes, uncertainties, and checkpoints."

Inference:：

Run retrieval_indatabase.py (single-GPU training) Modify the parameters according to your own situation. We provide the pre-trained weights of the feature extraction network Attnsleep for EEG signal.(pre_attn.py)