Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

中文翻译：

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基于 EEG 的感觉运动脑机接口的受试者内和受试者间变异性：综述

用于康复运动障碍的脑机接口 (BCI) 利用脑电图 (EEG) 中的感觉运动节律 (SMR)。然而，支撑 SMR 的神经生理学过程通常会随着时间和不同受试者而变化。固有的受试者内和受试者间可变性导致数据分布的协变量偏移，从而阻碍模型参数在会话/受试者之间的可转移性。迁移学习包括基于机器学习的方法，以补偿在 EEG 衍生特征分布中表现为 BCI 协变量偏移的受试者间和会话间（受试者内）可变性。除了迁移学习方法外，最近的研究还探索了心理和神经生理学预测因素以及学科间关联性评估，这可能会增强基于 EEG 的 BCI 中的迁移学习。这里，