卷积神经网络
计算机科学
人工智能
人工神经网络
深度学习
情绪分类
机器学习
价(化学)
唤醒
模式识别(心理学)
量子力学
生物
物理
神经科学
作者
Samarth Tripathi,Sneha Acharya,Ranti Dev Sharma,Sudhanshu Mittal,Samit Bhattacharya
出处
期刊:Innovative Applications of Artificial Intelligence
日期:2017-02-08
卷期号:: 4746-4752
被引量:18
摘要
Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.
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