Selection Method of IMFs Based on Noise-Assisted MEMD and Relevant Information

Liu, Tong; Zhu, Yifeng; Wang, Lequan; Wang, Chenglong

doi:10.1109/eiecs53707.2021.9587901

Cited by 1 publication

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“…In recent years, many efficient channel selection algorithms have emerged, such as correlation-based methods, machine-learning-based methods, wrapper-based methods, heuristic-searching-based methods, and so on. Correlation-based method (Park and Chung, 2020 ; Liu T. et al, 2021 ; Tiwari and Chaturvedi, 2021 ) goes through the EEG signals obtained from each channel and uses various information-theoretic concepts to evaluate the correlated channels for each feature and to select these channels. So far, some information theoretic concepts, such as normalized mutual information (NMI) (Yang et al, 2021 ), entropy (Ghembaza and Djebbari, 2022 ), correlation coefficient (Jin et al, 2019 ; Moon et al, 2020 ), and chi-squared statistics (Baig et al, 2020 ), have been used to assess the correlation of channels.…”

Section: Introductionmentioning

confidence: 99%

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Liu,

2023

Front. Neurosci.

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BackgroundFor non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.MethodsThe multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.ResultsThe proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.ConclusionThe formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

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Section: Introductionmentioning

confidence: 99%

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Liu,

2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

Selection Method of IMFs Based on Noise-Assisted MEMD and Relevant Information

Cited by 1 publication

References 5 publications

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

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