TY - GEN
T1 - Generating EEG Graphs Based on PLA for Brain Wave Pattern Recognition
AU - Zhang, Hao Lan
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
KW - Data Mining
KW - EEG Pattern Recognition
KW - Machinery Control
UR - http://www.scopus.com/inward/record.url?scp=85056282715&partnerID=8YFLogxK
U2 - 10.1109/CEC.2018.8477796
DO - 10.1109/CEC.2018.8477796
M3 - Conference contribution
AN - SCOPUS:85056282715
T3 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PB - IEEE
T2 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018
Y2 - 8 July 2018 through 13 July 2018
ER -