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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 August 2015, Santiago, Chile.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 The Authors
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set2013CB329304Chinese 973 Program
Not Set2014CB744604Chinese 973 Program
Not Set2015AA015403Chinese 863 Program
Not Set61272265Natural Science Foundation of China
Not Set61402324Natural Science Foundation of China
Keywords: emotion recognition; EEG; deep feature learning
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 44132
Depositing User: Dawei Song
Date Deposited: 24 Aug 2015 09:34
Last Modified: 09 Oct 2017 13:48
URI: http://oro.open.ac.uk/id/eprint/44132
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