用简单特征选对源数据，提升脑机接口跨用户迁移效果
Source Data Selection for Brain-Computer Interfaces based on Simple Features
- 基于数据协方差矩阵与黎曼距离，提取校准期简单特征
- 所提方法在公开数据集上优于其他源数据选择策略
- 适合需要快速适配新用户的脑机接口系统开发者
本文证明，在脑机接口校准阶段即可利用简单特征进行源数据选择，通过迁移学习显著提升新用户的表现。为此，研究使用公开的运动想象数据集进行分析，并提出一种名为转移性能预测器（Transfer Performance Predictor）的方法。该方法基于数据的协方差矩阵及其间的黎曼距离构建简单特征。实验表明，该方法能更准确地选出有助于目标用户迁移学习的源数据，性能优于现有其他源数据选择方法。
原文摘要 · Abstract (English)
This paper demonstrates that simple features available during the calibration of a brain-computer interface can be utilized for source data selection to improve the performance of the brain-computer interface for a new target user through transfer learning. To support this, a public motor imagery dataset is used for analysis, and a method called the Transfer Performance Predictor method is presented. The simple features are based on the covariance matrices of the data and the Riemannian distance between them. The Transfer Performance Predictor method outperforms other source data selection methods as it selects source data that gives a better transfer learning performance for the target users.
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