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首页> 外文期刊>American journal of applied sciences >Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces
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Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

机译：单次和多次基于P300的脑计算机接口的预处理和分类技术的比较

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The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial) as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial format (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-trial P300-based BCI systems, particularly for selecting appropriate pre-processing agents and classification methodologies for inclusion. The possibilities for future study include the investigation of double-trial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches will still be significant. It is very likely that such systems would benefit from accuracies higher than the one obtained in this study for single-trial LDA (74.19%).
机译：事件相关脑电势（ERP）的P300组件通常在脑计算机接口（BCI）中使用，以将个人的意图转换为外部设备的命令。但是，P300响应位于高背景噪声的信号环境中。因此，开发基于P300的BCI的主要问题在于在存在这种噪声的情况下识别P300响应。传统上，通过平均记录信号的多次试验来减弱P300数据的背景活性。这种方法虽然有效，但有两个缺点。首先，收集多个数据试验非常耗时，并且会延迟BCI响应。其次，由于在各个试验中P300的可变时间锁定，平均结果中可能会出现延迟失真。问题陈述：使用单次试验P300数据克服了这两个缺点。但是，单次试验数据必须正确去噪，以确保可靠的BCI操作。已使用多种信号处理技术和分类方法实现了基于P300的单次试验BCI。但是，将这些系统的准确性与其他多重试用系统进行比较可能会包含更多的比较，而不仅仅是试用格式（单一试用/多重试用），因为数据质量和记录情况可能会有所不同。方法：通过比较三种不同的预处理代理和三种分类方法对同一数据集的单次试验和多次试验设置的性能比较，直接解决了此问题。 BCI竞赛II的P300数据集用于促进这种比较。结果：LDA分类器在对单次尝试（74.19％）和多次尝试（100％）格式的未见P300时空特征进行分类中表现出最佳性能。就计算和内存要求而言，它也是非常有效的。结论：本研究可以为从业人员开发基于P300的单项和多项审判的BCI系统提供一般指导，尤其是选择合适的预处理剂和分类方法进行纳入时。未来研究的可能性包括研究基于LDA分类器的双审和三审P300系统。这样的方法所节省的时间仍将是可观的。这样的系统很有可能会比本研究中获得的单次试验LDA的准确性更高（74.19％）。

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