Study on building subject-specific classifiers based on EEG data

Author(s): L. Pang^{1, 2} ; X. Wang¹ ; J. Zhang¹ ; X. Wanyan¹ ; X. Wang³ ; J. Liang³ ; L. Zhang³
DOI: 10.1049/icp.2021.0445

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Author(s): L. Pang^{1, 2} ; X. Wang¹ ; J. Zhang¹ ; X. Wanyan¹ ; X. Wang³ ; J. Liang³ ; L. Zhang³
- Affiliations: 1: School of Aviation Science and Engineering , Beihang University , Beijing 100191, China ;
  2: School of Aero-engine , Shenyang Aerospace University , Shenyang 110136, China ;
  3: Marine Human Factors Engineering Lab , China Institute of Marine Technology & Economy , Beijing 100081, China

Source:

CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020),

2021

683 – 687

Conference: CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020)

DOI: 10.1049/icp.2021.0445

ISBN: 978-1-83953-419-5

Location: Online Conference

Conference date: 18-21 September 2020

Format: PDF

In a modern human-machine system, it is very meaningful to study the subject-specific mental workload classification for the increasing high mental workload. In this paper, EEG data is used to build the Subject-specific Classifiers (SSCs) based on Stochastic Configuration Network (SCN). SCN has the advantage of random neural network, and can realize the hyper-parameters setting. The results show that the SSCs built in this paper perform well, the range of SSCs test accuracies is between 56.5% and 90.2% with an average of 75.9%.

Inspec keywords: medical signal processing; signal classification; bioelectric potentials; stochastic processes; electroencephalography; neural nets

Subjects: Neural nets; Electrodiagnostics and other electrical measurement techniques; Bioelectric signals; Signal processing and detection; Biology and medical computing; Digital signal processing; Other topics in statistics; Electrical activity in neurophysiological processes

Study on building subject-specific classifiers based on EEG data

Study on building subject-specific classifiers based on EEG data

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