Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
Department of Neurology, Rigshospitalet, Glostrup, 2600, Denmark.
Comput Biol Med. 2018 Dec 1;103:24-33. doi: 10.1016/j.compbiomed.2018.09.021. Epub 2018 Oct 11.
This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.
本文描述了一种使用共空间模式（CSP）滤波器结合递归最小二乘（RLS）方法来迭代更新 CSP 滤波器系数的脑机接口（BCI）方案的实现。通过使用对角加载（DL）引入正则化，使提出的自适应 CSP（ACSP）算法更加稳健，从而能够在向 BCI 系统引入新患者时显著减少训练阶段的长度。该系统在来自 BCI 竞赛 IV（2a）的 4 类多肢体运动想象（MI）数据集和内部记录的更复杂的单肢体 3 类 MI 数据集上进行了测试。后者数据集旨在模拟上肢康复治疗，例如中风后。研究结果表明，当有大量校准数据时，ACSP 的性能与 CSP 相当（4 类问题的kappa 值分别为 0.523 和 0.502）；对于校准阶段较少的情况，ACSP 显著提高了系统的性能（高达 4 倍）。所提出的范例证明是可行的，ACSP 算法似乎能够实现用户或半用户独立的场景，其中消除了无需反馈的长时间系统校准阶段的需求。
