Intent recognition in smart living through deep recurrent neural networks

Xiang Zhang*, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

52 Citations (Scopus)


Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).

Original languageEnglish
Title of host publication24th International Conference on Neural Information Processing (ICONIP 2017) : proceedings
EditorsDerong Liu, Dongbin Zhao, Shengli Xie, El-Sayed M. El-Alfy, Yuanqing Li
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages11
ISBN (Electronic) 9783319700960
ISBN (Print)9783319700953
Publication statusPublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues


Conference24th International Conference on Neural Information Processing, ICONIP 2017


  • Deep learning
  • EEG
  • Intent recognition
  • Smart home


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