This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.

Emotion Recognition from EEG Signals using the DEAP dataset with 86.4% accuracy. Applied multiple machine learning models and implemented various signal transforming algorithms like the DWT algorithm.

基于Tensorflow，OpenCV. 使用MNIST数据集训练卷积神经网络模型，用于手写数字识别

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台湾大学李宏毅老师机器学习

EEG Emotion classification using the DEAP pre-processed data