基于SSVEP的脑机接口最新算法的比较评估
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
摘要：
脑机接口（BCI）在使人机交互更加自然方面取得了越来越大的进展，特别是对于神经肌肉残疾的人。在现有的解决方案中，依靠脑电图（EEG）的系统由于其非侵入性而占据了最突出的位置。然而，将EEG信号转换为计算机命令的过程远非微不足道，因为它需要优化许多不同的参数，这些参数需要联合调整。在这份报告中，我们专注于基于EEG的脑机接口依赖于稳态视觉诱发电位（SSVEP）的类别，并进行比较评估的最有前途的算法在文献中。更具体地说，我们为构成BCI系统的各种不同参数中的每一个定义了一组算法（即，过滤、伪影去除、特征提取、特征选择和分类），并通过保持所有其他参数固定来独立地研究每个参数。从该评估过程中获得的结果与由11名受试者的256通道EEG信号组成的数据集以及用于再现结果并支持进一步实验的处理工具箱一起提供。通过这种方式，我们设法为社区提供了一个最先进的基于SSVEP的BCI基线，可以作为引入新方法和途径的基础。
英文摘要：
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this report, we focus on the category of EEG-based BCIs that rely on Steady-State-Visual-Evoked Potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. More specifically, we define a set of algorithms for each of the various different parameters composing a BCI system (i.e. filtering, artifact removal, feature extraction, feature selection and classification) and study each parameter independently by keeping all other parameters fixed. The results obtained from this evaluation process are provided together with a dataset consisting of the 256-channel, EEG signals of 11 subjects, as well as a processing toolbox for reproducing the results and supporting further experimentation. In this way, we manage to make available for the community a state-of-the-art baseline for SSVEP-based BCIs that can be used as a basis for introducing novel methods and approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
