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Author:

Lian, Zhaoyang (Lian, Zhaoyang.) | Duan, Lijuan (Duan, Lijuan.) (Scholars:段立娟) | Qiao, Yuanhua (Qiao, Yuanhua.) | Chen, Juncheng (Chen, Juncheng.) | Miao, Jun (Miao, Jun.) | Li, Mingai (Li, Mingai.)

Indexed by:

EI Scopus SCIE

Abstract:

The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset.

Keyword:

Electroencephalography improved bionic whale optimization algorithms optimization of extreme learning machine Optimization Brain-computer interfaces WOA-ELM Image segmentation Sociology EEG signals classification Brain computer interface Data mining Feature extraction

Author Community:

  • [ 1 ] [Lian, Zhaoyang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Chen, Juncheng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Mingai]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Lian, Zhaoyang]Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China
  • [ 6 ] [Duan, Lijuan]Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China
  • [ 7 ] [Lian, Zhaoyang]Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China
  • [ 8 ] [Duan, Lijuan]Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China
  • [ 9 ] [Qiao, Yuanhua]Beijing Univ Technol, Appl Sci, Beijing 100124, Peoples R China
  • [ 10 ] [Miao, Jun]Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing Key Lab Internet Culture & Digital Dissem, Beijing 100101, Peoples R China

Reprint Author's Address:

  • 段立娟

    [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Duan, Lijuan]Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China;;[Duan, Lijuan]Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China

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Source :

IEEE ACCESS

ISSN: 2169-3536

Year: 2021

Volume: 9

Page: 67405-67416

3 . 9 0 0

JCR@2022

JCR Journal Grade:2

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count: 19

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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