Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition

Hao Lan Zhang; Huanyu Zhao; Yiu Ming CHEUNG; Jing He

doi:10.1109/CEC.2018.8477796

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

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477796
Publication status	Published - 28 Sep 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Scopus Subject Areas

Artificial Intelligence
Control and Optimization

User-Defined Keywords

BCI
Data Mining
EEG Pattern Recognition
Machinery Control

Access to Document

10.1109/CEC.2018.8477796

Cite this

Zhang, H. L., Zhao, H., CHEUNG, Y. M., & He, J. (2018). Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings [8477796] (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477796

@inproceedings{bf4f1ad876a448e6859ac543692d8258,

title = "Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition",

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.",

keywords = "BCI, Data Mining, EEG Pattern Recognition, Machinery Control",

author = "Zhang, {Hao Lan} and Huanyu Zhao and CHEUNG, {Yiu Ming} and Jing He",

note = "Funding Information: China (No. 61272480, No. 61572022), Zhejiang Natural Science Fund (No.LY14G010004, LY18F020001). Thanks to the EEG Lab at Nottingham University (China) for providing some EEG data sources, which will be further evaluated by SEGPA. Funding Information: This work is partially supported by Ningbo Innovation Team (No. 2016C11024), National Natural Science Fund of; 2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = sep,

day = "28",

doi = "10.1109/CEC.2018.8477796",

language = "English",

series = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",

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Zhang, HL, Zhao, H, CHEUNG, YM & He, J 2018, Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition. in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings., 8477796, 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/CEC.2018.8477796

Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition. / Zhang, Hao Lan; Zhao, Huanyu; CHEUNG, Yiu Ming; He, Jing.

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477796 (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

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AU - Zhao, Huanyu

AU - CHEUNG, Yiu Ming

AU - He, Jing

N1 - Funding Information: China (No. 61272480, No. 61572022), Zhejiang Natural Science Fund (No.LY14G010004, LY18F020001). Thanks to the EEG Lab at Nottingham University (China) for providing some EEG data sources, which will be further evaluated by SEGPA. Funding Information: This work is partially supported by Ningbo Innovation Team (No. 2016C11024), National Natural Science Fund of

PY - 2018/9/28

Y1 - 2018/9/28

N2 - 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.

AB - 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.

KW - BCI

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M3 - Conference contribution

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BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition

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