# MLSEED
This project applies different kinds of ML algorithms on the SEED Dataset to classify the data into 3 states: Positive Emotion, Neutral, Negative Emotion.
# Getting started
The algorithms in this repo are applied to the 5th dimension (gamma band which is best for emotion recognition) of “de_LDS” features of the Dataset. There are 62 inputs (feature vectors) in each de_LDS for every subject of the experiment. All de_LDS features contribute to
more than 150000 samples in the dataset. The objective is to train models to map them using 62 feature vectors to 3 labels.
# Prerequisites
The project is written in python using anaconda in jupyter notebook. Some of the libraries youl'll need in the project are:
Tensorflow,
sk-Learn,
Numpy,
Pandas,
Matplotlib and
Seaborn.
# Author
Rasoul Ghaznavi
# Licence
This project is licensed under the GNU General Public License v3.0.
基于seed数据集的脑电情绪识别（机器学习算法）
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机器学习的seed脑电情绪识别 Docs 
Algorithm Comparison.docx 18KB Notebooks Random Forest 
Random Forest with PCA.ipynb 6KB Random Forest.ipynb 4KB K Nearest Neighbors KNN.ipynb 3KB KNN with PCA.ipynb 6KB Decision Tree Decision Tree.ipynb 3KB Decision Tree with PCA.ipynb 6KB SVM Sigmoid SVM with PCA.ipynb 6KB Polynomial SVM with PCA.ipynb 6KB Polynomial SVM.ipynb 3KB RBF SVM with PCA.ipynb 6KB Linear SVM.ipynb 3KB Linear SVM with PCA.ipynb 6KB RBF SVM.ipynb 3KB Sigmoid SVM.ipynb 3KB Gaussian Naive Bayes Gaussian Naive Bayes with PCA.ipynb 6KB Gaussian Naive Bayes.ipynb 3KB LICENSE 34KB .gitignore 1KB README.md 894B
readme.txt 558B
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