Converting your thoughts to texts: enabling brain typing via deep feature learning of EEG signals

Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, Dalin Zhang

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

32 Citations (Scopus)

Abstract

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large-scale public MI-EEG dataset and a limited but easy-To-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-The-Art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-10
Number of pages10
ISBN (Print)9781538632246
DOIs
Publication statusPublished - 22 Aug 2018
Event2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018 - Athens, Greece
Duration: 19 Mar 201823 Mar 2018

Conference

Conference2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018
CountryGreece
CityAthens
Period19/03/1823/03/18

Keywords

  • BCI
  • brain typing
  • deep learning
  • EEG

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