PDF(Pubmed)
Abstract：
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.
摘要：
运动图像脑机接口通过脑电图（EEG）连接人脑和计算机。然而,运动想象任务期间大脑活动频率范围的个体差异构成了挑战，限制了运动图像分类的手动特征提取。要提取与特定主题匹配的特征，我们提出了一种新颖的运动图像分类模型，该模型使用具有自适应结构LASSO的独特特征融合。具体来说,我们从脑电信号的重叠和多尺度子带中提取了空间域特征，并通过将特征与空间信息的任务相关性融合到基于自适应LASSO的特征选择中来挖掘判别特征。我们在公共运动想象脑电图数据集上评估了所提出的模型，证明该模型具有优异的性能。同时,所提出模型的消融研究和特征选择可视化进一步验证了脑电分析的巨大潜力。
