IEEE Trans Neural Syst Rehabil Eng. 2020 Dec;28(12):2754-2761. doi: 10.1109/TNSRE.2020.3043418. Epub 2021 Jan 28.
The P300 wave is commonly used in Brain-Computer Interface technology due to its higher bit rates when compared to other BCI paradigms. P300 classification pipelines based on Riemannian Geometry provide accuracies on par with state-of-the-art pipelines, without having the need for spatial filters, and also possess the ability to be calibrated with little data. In this study, five different P300 detection pipelines are compared, with three of them using Riemannian Geometry as either feature extraction or classification algorithms. The goal of this study is to assess the viability of Riemannian Geometry-based methods in non-optimal environments with sudden background noise changes, rather than maximizing classification accuracy values. For fifteen subjects, the average single-trial accuracy obtained for each pipeline was: 56.06% for Linear Discriminant Analysis (LDA), 72.13% for Bayesian Linear Discriminant Analysis (BLDA), 63.56% for Riemannian Minimum Distance to Mean (MDM), 69.22% for Riemannian Tangent Space with Logistic Regression (TS-LogR), and 63.30% for Riemannian Tangent Space with Support Vector Machine (TS-SVM). The results are higher for the pipelines based on BLDA and TS-LogR, suggesting that they could be viable methods for the detection of the P300 component when maximizing the bit rate is needed. For multiple-trial classification, the BLDA pipeline converged faster towards higher average values, closely followed by the TS-LogR pipeline. The two remaining Riemannian methods' accuracy also increases with the number of trials, but towards a lower value compared to the aforementioned ones. Single-stimulus detection metrics revealed that the TS-LogR pipeline can be a viable classification method, as its results are only slightly lower than those obtained with BLDA. P300 waveforms were also analyzed to check for evidence of the component being elicited. Finally, a questionnaire was used to retrieve the most intuitive focusing methods employed by the subjects.
P300 波常用于脑机接口技术，因为与其他 BCI 范式相比，它具有更高的比特率。基于黎曼几何的 P300 分类管道提供了与最先进管道相当的精度，而无需使用空间滤波器，并且还具有可以用少量数据进行校准的能力。在这项研究中，比较了五种不同的 P300 检测管道，其中三种使用黎曼几何作为特征提取或分类算法。本研究的目的是评估基于黎曼几何的方法在突发背景噪声变化的非最佳环境中的可行性，而不是最大化分类准确性值。对于十五名受试者，每个管道获得的平均单次试验准确性为：线性判别分析（LDA）为 56.06%，贝叶斯线性判别分析（BLDA）为 72.13%，黎曼最小距离均值（MDM）为 63.56%，黎曼切空间与逻辑回归（TS-LogR）为 69.22%，黎曼切空间与支持向量机（TS-SVM）为 63.30%。基于 BLDA 和 TS-LogR 的管道结果更高，表明在需要最大化比特率时，它们可能是检测 P300 分量的可行方法。对于多次试验分类，BLDA 管道更快地收敛到更高的平均值，紧随其后的是 TS-LogR 管道。另外两种基于黎曼的方法的准确性也随着试验次数的增加而增加，但与前两者相比，准确性较低。单刺激检测指标表明，TS-LogR 管道可以是一种可行的分类方法，因为其结果仅略低于 BLDA 获得的结果。还分析了 P300 波形以检查所激发组件的证据。最后，使用问卷检索受试者使用的最直观的聚焦方法。
