Public Models considered for emotion estimation from EEG

Last update: Dec 23, 2022

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Overview

Emotion-EEG

Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with the help of a saliency analysis.

Instruction

The three proposed models are direcly available here:

Saliency based combination of the hierarchical RNN and CNN. Sal_Model.py
Feature fusion of the hierarchical RNN and CNN. Feat_Model.py Sal_Model* Ouput fusion of the hierarchical RNN and CNN. Loss_Model.py

Installation and Dependencies

Pytorch 1.5

MNE

Cuda 10.1

Installation with pip: pip install -r req.txt

Import of the environment with conda: conda env create -f env.yml

Remarks

Due to the EULA for each dataset, some example signals have been proposed to test the models, however, they are not corresponding to signals from one of tested dataset.

If you are interested in our work, don't hesitate to contact us.

Best! 😄

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Comments

Runtime error on DEAP Dataset

Hi,

I've tried to run the Sal_model.py on DEAP, but apart from fixing the number of output units of the classifier (using 4 for the valence/arousal quadrants) I can't get it to work because of an error:

RuntimeError: shape '[168, 5, 1, -1]' is invalid for input of size 43352064 The size is the product of 8064x32x168 (at least that's what it seems), of time samples x channels x (batch_size=128 + 40?)

The line is the following: https://github.com/numediart/Emotion-EEG/blob/master/Models_DEAP.py#L530

Also I implemented the feature generation part myself, as it was incomplete (only the sensor locations code conversion is in the repo), by using "mne.time_frequency.psd_array_welch" and then averaging the power values of 5 frequency bins that are mentioned in the DEAP paper (for a total of 5 image channels which should be correct).

Best Regards.

opened by ale-pavel 0
about the code

I used your code to run, but it reported an error, it shows such an error in the classifier section： “”RuntimeError: Given groups=1, weight of size [32, 5, 3, 3], expected input[144, 3, 32, 32] to have 5 channels, but got 3 channels instead“” please reply me if you see it, thank you

opened by lwlBCI 4

Owner

Victor Delvigne

PhD candidate at @numediart. My research project focuses on the used of innovative technologies in the context of biomedical signal processing.

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