Objective: BCI (Brain-Computer Interface) technology operates in three modes: online, offline, and pseudo-online. In the online mode, real-time EEG data is constantly analyzed. In offline mode, the signal is acquired and processed afterwards. The pseudo-online mode processes collected data as if they were received in real-time. The main difference is that the offline mode often analyzes the whole data, while the online and pseudo-online modes only analyze data in short time windows. Offline analysis is usually done with asynchronous BCIs, which restricts analysis to predefined time windows. Asynchronous BCI, compatible with online and pseudo-online modes, allows flexible mental activity duration. Offline processing tends to be more accurate, while online analysis is better for therapeutic applications. Pseudo-online implementation approximates online processing without real-time constraints. Many BCI studies being offline introduce biases compared to real-life scenarios, impacting classification algorithm performance. Approach: The objective of this research paper is therefore to extend the current MOABB framework, operating in offline mode, so as to allow a comparison of different algorithms in a pseudo-online setting with the use of a technology based on overlapping sliding windows. To do this will require the introduction of a idle state event in the dataset that takes into account all different possibilities that are not task thinking. To validate the performance of the algorithms we will use the normalized Matthews Correlation Coefficient (nMCC) and the Information Transfer Rate (ITR). Main results: We analyzed the state-of-the-art algorithms of the last 15 years over several Motor Imagery (MI) datasets composed by several subjects, showing the differences between the two approaches from a statistical point of view. Significance: The ability to analyze the performance of different algorithms in offline and pseudo-online modes will allow the BCI community to obtain more accurate and comprehensive reports regarding the performance of classification algorithms.

翻译：目的：脑机接口（BCI）技术存在三种运行模式：在线、离线与伪在线。在线模式下需持续分析实时脑电图信号；离线模式下信号采集与分析均在事后完成；伪在线模式则将已采集数据模拟为实时接收进行处理。其主要差异在于：离线模式通常对整体数据进行分析，而在线与伪在线模式仅分析短时窗内的数据。离线分析常用于异步BCI系统，这将其分析范围限定在预定义时间窗口。支持在线与伪在线模式的异步BCI则允许灵活的心理活动持续时间。离线处理通常具有更高的准确率，而在线分析更适用于治疗应用场景。伪在线实现可在无实时约束条件下近似在线处理过程。当前多数BCI研究采用离线模式，这相较于真实应用场景存在偏差，进而影响分类算法性能。方法：本研究旨在扩展当前仅支持离线模式的MOABB框架，通过引入基于重叠滑动窗口的技术，使其能够在伪在线设定下实现不同算法的对比。为此需要在数据集中增加空闲状态事件，该事件需涵盖所有非任务思维的可能性。我们将采用标准化马修斯相关系数（nMCC）与信息传输速率（ITR）进行算法性能验证。主要结果：我们分析了近15年涵盖多个受试者的运动想象（MI）数据集上的最新算法，从统计学角度揭示了两种模式间的差异。意义：通过分析不同算法在离线与伪在线模式下的性能表现，将有助于BCI领域研究者获得更精确全面的分类算法性能评估报告。