论文标题
转移学习提高了MI BCI模型的分类精度
Transfer Learning improves MI BCI models classification accuracy in Parkinson's disease patients
论文作者
论文摘要
基于汽车的BCI（MI-BCI）神经康复可以提高运动能力并减轻帕金森氏病患者的缺陷症状。需要先进的运动构想BCI方法来克服此类患者的准确性和与时间相关的MI BCI校准挑战。在这项研究中，我们提出了基于课际转移学习的多课程FBCSP（MSFBCSP），并研究了与单课的FBSCP相比的性能。这项研究的主要结果是，与PD患者的单会FBCSP相比，提议的MSFBCSP获得了明显提高的精度（中位数为81.3％，范围为41.2-100.0％，分别为61.1％，范围为25.0-100.0.0％； p <0.001）。总之，这项研究提出了一种基于转移学习的基于多学业的方法，该方法允许在PD患者对PD患者进行的MI BCI中显着提高校准精度。
Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotor ability and reduce the deficit symptoms in Parkinson's Disease patients. Advanced Motor-Imagery BCI methods are needed to overcome the accuracy and time-related MI BCI calibration challenges in such patients. In this study, we proposed a Multi-session FBCSP (msFBCSP) based on inter-session transfer learning and we investigated its performance compared to the single-session based FBSCP. The main result of this study is the significantly improved accuracy obtained by proposed msFBCSP compared to single-session FBCSP in PD patients (median 81.3%, range 41.2-100.0% vs median 61.1%, range 25.0-100.0%, respectively; p<0.001). In conclusion, this study proposes a transfer learning-based multi-session based FBCSP approach which allowed to significantly improve calibration accuracy in MI BCI performed on PD patients.
