Weihai Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Weihai Huang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Qiyan Sun
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Weize Yang
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication - Xiangzeng Kong
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
1 shared publication - Xinyue Liu
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
1 shared publication - Yihua Li
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, China
1 shared publication
Latest Publications
- Open Access
Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO
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.
Open AccessMotor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO
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.