Evaluation on game concentration with multi-scale fuzzy entropy based on EEG signals

Wang, Youcheng; Huang, Qingxu; Xie, Zun; Wang, Meng; Bao, Wei

doi:10.1016/j.entcom.2023.100570

Cited by 6 publications

(1 citation statement)

References 47 publications

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“…In BCI systems, motor imagery based on electroencephalography (EEG) represents one of the most pivotal patterns. By analyzing the physiological information related to limb movements in EEG signals, the motor imagery BCI realizes brain–computer communication and can be used in auxiliary rehabilitation training [ 2 , 3 , 4 ], virtual games [ 5 ], navigation [ 6 ] and other scenarios. Given the broad prospects, the motor imagery BCI has attracted widespread attention around the world.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO

Huang,

Liu,

Yang

et al. 2024

Sensors

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A motor imagery brain–computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

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