[论文解读] End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities

Quentin Barthélemy, Sylvain Chevallier+2 more|arXiv (Cornell University)|Mar 15, 2022

EEG and Brain-Computer Interfaces参考文献 45被引用 6

一句话总结

该论文提出 ASAP（贝叶斯黎曼概率累积），一种端到端的 P300 BCI，通过使用基于概率的黎曼 MDM 进行 ERP 检测，并利用贝叶斯推断在试验间累积置信度（同时更新目标和非目标闪光），从而提升字符分类性能。在公开数据集上，与最先进方法相比，ASAP 以更少的重复次数实现了显著更高的准确率和信息传输速率（ITR）。

ABSTRACT

In brain-computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a speller task. While this is an important objective, existing P300 BCI still require several repetitions to achieve a correct classification accuracy. Signal processing and machine learning advances in P300 BCI mostly revolve around the P300 detection part, leaving the character classification out of the scope. To reduce the number of repetitions while maintaining a good character classification, it is critical to embrace the full classification problem. We introduce an end-to-end pipeline, starting from feature extraction, and is composed of an ERP-level classification using probabilistic Riemannian MDM which feeds a character-level classification using Bayesian accumulation of confidence across trials. Whereas existing approaches only increase the confidence of a character when it is flashed, our new pipeline, called Bayesian accumulation of Riemannian probabilities (ASAP), update the confidence of each character after each flash. We provide the proper derivation and theoretical reformulation of this Bayesian approach for a seamless processing of information from signal to BCI characters. We demonstrate that our approach performs significantly better than standard methods on public P300 datasets.

研究动机与目标

• 为解决 P300 BCI 中字符分类滞后于 ERP 检测的问题，该问题源于对重复闪光的依赖。

提出的方法

• 采用基于原型的协方差矩阵，将 EEG 时段在黎曼空间中表示，以实现稳健的 ERP 特征提取。

实验结果

研究问题

• RQ1 贝叶斯黎曼概率累积是否能够使 P300 BCI 性能超越标准 ERP 检测流程？

主要发现

• ASAP 在所有测试数据集上均显著优于 MDM+OM、xDAWN+OM 和 RegLDA+OM，实现了更高的信息传输速率（ITR）。