Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces

作     者：Abibullaev, Berdakh Zollanvari, Amin 

作者机构：Nazarbayev Univ Dept Robot & Mech Astana 010000 Kazakhstan Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan 

出 版 物：《IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS》 (IEEE J. Biomedical Health Informat.)

年 卷 期：2019年第23卷第5期

页      面：2009-2020页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Nazarbayev University Faculty Development [SOE2018008] 

主　　题：Brain-computer interfaces ERPs P300 EEG signal processing machine learning 

摘      要：Constructing accurate predictive models is at the heart of brain-computer interfaces (BCIs) because these models can ultimately translate brain activities into communication and control commands. The majority of the previous work in BCI use spatial, temporal, or spatiotemporal features of event-related potentials (ERPs). In this study, we examined the discriminatory effect of their spatiospectral features to capture the most relevant set of neural activities from electroencephalographic recordings that represent users mental intent. In this regard, we model ERP waveforms using a sum of sinusoids with unknown amplitudes, frequencies, and phases. The effect of this signal modeling step is to represent high-dimensional ERP waveforms in a substantially lower dimensionality space, which includes their dominant power spectral contents. We found that the most discriminative frequencies for accurate decoding of visual attention modulated ERPs lie in a spectral range less than 6.4 Hz. This was empirically verified by treating dominant frequency contents of ERP waveforms as feature vectors in the state-of-the-art machine learning techniques used herein. The constructed predictive models achieved remarkable performance, which for some subjects was as high as 94% as measured by the area under curve. Using these spectral contents, we further studied the discriminatory effect of each channel and proposed an efficient strategy to choose subject-specific subsets of channels that generally led to classifiers with comparable performance.