EEG Based Emotion Identification Using Unsupervised Deep Feature Learning

Li, Xiang; Zhang, Peng; Song, Dawei; Yu, Guangliang; Hou, Yuexian and Hu, Bin (2015). EEG Based Emotion Identification Using Unsupervised Deep Feature Learning. In: SIGIR2015 Workshop on Neuro-Physiological Methods in IR Research, 13 Aug 2015, Santiago, Chile.

URL: https://docs.google.com/viewer?a=v&pid=sites&srcid...

Abstract

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition.

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About

• Item ORO ID
• 44132
• Item Type
• Conference or Workshop Item
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Not Set	2013CB329304	Chinese 973 Program
  Not Set	2014CB744604	Chinese 973 Program
  Not Set	2015AA015403	Chinese 863 Program
  Not Set	61272265	Natural Science Foundation of China
  Not Set	61402324	Natural Science Foundation of China

• Keywords
• emotion recognition; EEG; deep feature learning
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2015 The Authors
• Related URLs
• • https://sites.google.com/site/neuroir201...(Other)
  • http://sigir2015.org/(Other)
• Depositing User
• Dawei Song