减少长期用户脑机接口校准时间的迁移学习算法

Joshua Giles, K. Ang, K. Phua, M. Arvaneh
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引用次数: 3

摘要

当前基于运动图像的脑机接口(BCI)系统需要在每次会话开始时进行长时间的校准,才能以足够的分类精度水平使用。特别是,这个问题对于长期BCI用户来说可能是一个重大负担。本文提出了一种新的迁移学习算法,称为r-KLwDSA,以减少长期用户的BCI校准时间。提出的r-KLwDSA算法采用一种新颖的线性对齐方法,将用户在前几次会话中收集的EEG数据与当前会话中收集的少量EEG试验进行对齐。然后,通过加权机制将前一时段的对齐脑电图试验和当前时段的少量脑电图试验融合在一起,然后用于校准脑机接口模型。为了验证所提出的算法,使用了包含11例脑卒中患者脑电图数据的大型数据集,每例患者进行18次脑机接口。当当前会话中每个类只有两次试验时,与会话特定算法相比,所提出的框架在分类精度方面有了显着提高,超过4%。所提出的算法在提高初始会话特定准确度低于60%的会话的BCI准确度方面特别成功,平均准确度提高约10%,从而使更多的卒中患者获得有意义的BCI康复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Transfer Learning Algorithm to Reduce Brain-Computer Interface Calibration Time for Long-Term Users
Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation.
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