Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

脑机接口（BCI）是允许人们绕过周围神经系统（PNS）的天然神经肌肉和激素输出与环境进行交互的系统。这些界面记录用户的大脑活动，并将其转化为外部设备的控制命令，从而为PNS提供附加的人工输出。在此框架中，基于P300事件相关电位（ERP）的BCI已被证明是特别成功和强大的，该事件相关电位表示特定事件或刺激后从大脑记录的电反应。通过分类算法确定脑电图特征内P300诱发电位的存在与否。线性分类器（例如逐步线性判别分析和支持向量机（SVM））是用于ERPs分类的最常用的判别算法。由于EEG信号的信噪比低，因此在对信号进行分类之前，先执行多个刺激序列（又称迭代），然后求平均。但是，虽然增加迭代次数可以提高信噪比，但也会减慢该过程。在早期研究中，迭代次数是固定的（没有停止环境），但是最近在文献中提出了几种早期停止策略，当满足特定条件时可以动态中断刺激序列以提高通信速率。在这项工作中 我们探索如何通过结合优化和机器学习来提高基于P300的BCI中的分类性能。首先，我们提出了一个新的决策函数，旨在在不停机和提前停机的环境中提高准确性和信息传输率方面的分类性能。然后，我们提出了一个新的SVM训练问题，旨在促进目标检测过程。我们的方法被证明对几个公开可用的数据集有效。