Shahtalebi Soroosh, Mohammadi Arash
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:7161-7164. doi: 10.1109/EMBC.2019.8856340.
In contrary to recent signal/information processing advancements, human brain remains the most intriguing signal processing unit in existence with inconceivable capabilities to fuse various multi-modal signals, adaptively and in real-time fashion. To connect brain with the outer world, brain computer interfacing (BCI) via Electroencephalography (EEG) signals has received extensive attention. Extracting informative and discriminating features from EEG signals and decomposing the recorded signals into their underlying components is believed to yield compromising results. Different algorithms, therefore, are recently proposed combining signal decomposition techniques (e.g., spectral filterbanks, and Wavelet decomposition) with feature extracting methodologies (e.g., common spatial patterns (CSP), and Riemannian manifold learning). Although coupling filterbanks and Wavelet with the CSP has been investigated, to best of our knowledge, the potentials of coupling Wavelet with Riemannian manifold learning are not yet studied. The paper addresses this gap. In particular, we propose a level-based classification approach that couples the Wavelet decomposition with Riemannian manifold spatial learning (WvRiem). In the proposed WvRiem framework, the EEG signals are decomposed into several components (levels) and then spatial filtering via Riemannian manifold learning is performed on the best level which yields the most discriminating features. The proposed WvRiem is evaluated on the BCI Competition IV dataset and noticeably outperforms its counterparts.
与近期信号/信息处理技术的进展相反，人类大脑仍然是现存最引人入胜的信号处理单元，具有融合各种多模态信号的不可思议的能力，能够自适应地实时进行融合。为了将大脑与外部世界连接起来，通过脑电图（EEG）信号进行脑机接口（BCI）受到了广泛关注。人们认为，从EEG信号中提取信息丰富且具有区分性的特征，并将记录的信号分解为其潜在成分，会产生令人满意的结果。因此，最近提出了不同的算法，将信号分解技术（如频谱滤波器组和小波分解）与特征提取方法（如共同空间模式（CSP）和黎曼流形学习）相结合。尽管已经研究了将滤波器组和小波与CSP相结合的情况，但据我们所知，小波与黎曼流形学习相结合的潜力尚未得到研究。本文填补了这一空白。具体而言，我们提出了一种基于层次的分类方法，将小波分解与黎曼流形空间学习（WvRiem）相结合。在所提出的WvRiem框架中，EEG信号被分解为几个成分（层次），然后通过黎曼流形学习对产生最具区分性特征的最佳层次进行空间滤波。所提出的WvRiem在BCI竞赛IV数据集上进行了评估，并且明显优于同类方法。
