Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition

Hao Lan Zhang, Huanyu Zhao, Yiu Ming CHEUNG, Jing He*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Brain Computer Interface (BCI) has been an emerging topic in recent years. Specially, Artificial Intelligence (AI) is becoming a hot research area in recent years. However, many BCI techniques utilize invasive interfaces to brains (animal or human), which could cause potential risks for experimental subjects. EEG (Electroencephalography) technique has been used extensively as a non-invasive BCI solution for brain activity study. Many psychological work has suggested that human brains can generate some recognizable EEG signals associated with some specific activities. This paper suggests a novel EEG recognition method, i.e. Segmented EEG Graph using PLA (SEGPA), that incorporates improved Piecewise Linear Approximation (PLA) algorithm and EEG-based weighted network for EEG pattern recognition, which can be used for machinery control. The improved PLA algorithm and EEG-based weighted network technique incorporates the data sampling and segmentation method. This research proposes a potentially efficient method for recognizing human's brain activities that can be used for machinery or robot control.

Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060177
DOIs
Publication statusPublished - 28 Sep 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Scopus Subject Areas

  • Artificial Intelligence
  • Control and Optimization

User-Defined Keywords

  • BCI
  • Data Mining
  • EEG Pattern Recognition
  • Machinery Control

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