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Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm

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Abstract

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left–right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance.

Graphical abstract

Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62073282, 11832009), the Central Guidance on Local Science and Technology Development Fund of Hebei Province (Grant No. 206Z0301G), Natural Science Foundation of Hebei Province (Grant Nos. F2022203092, F2020203061), the Full-time Introduction of National High level Innovation Talents Research Project of Hebei Province (Grant No. 2021HBQZYCSB003).

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Authors and Affiliations

  1. Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Yushan Du, Jiaxin Sui & Rongrong Fu

  2. Jiangxi New Energy Technology Institute, Xinyu, 338000, China

    Shiwei Wang

  3. Department of Electrical, Computer & Biomedical Engineering, Ryerson University, Toronto, Canada

    Chengcheng Jia

Authors
  1. Yushan Du
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  2. Jiaxin Sui
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  3. Shiwei Wang
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  4. Rongrong Fu
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Contributions

Yushan Du and Jiaxin Sui contributed to the conceptualization, systematic review of articles, methodology development, formal analysis, and original writing. Shiwei Wang verified the experimental results and undertook project management. Rongrong Fu participated in the conceptualization, methodology and correction. Chengcheng Jia contributed to the external supervision and correction. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rongrong Fu.

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Highlights

(1) Aiming at the limitation of single feature extraction, an analysis method based on the combination of brain functional network and CSP is proposed.

(2) Through feature fusion and dimension reduction, low-dimensional information features with strong discriminative ability are obtained.

(3) Compared with the traditional method, the classification performance of motor imagery EEG signals is improved.

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Cite this article

Du, Y., Sui, J., Wang, S. et al. Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm. Med Biol Eng Comput 61, 2665–2676 (2023). https://doi.org/10.1007/s11517-023-02878-z

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