Optimization of data pre-processing methods for time-series classification of electroencephalography data

The performance of time-series classification of electroencephalographic data varies strongly across experimental paradigms and study participants. Reasons are task-dependent differences in neuronal processing and seemingly random variations between subjects, amongst others. The effect of data pre-processing techniques to ameliorate these challenges is relatively little studied. Here, the influence of spatial filter optimization methods and non-linear data transformation on time-series classification performance is analyzed by the example of high-frequency somatosensory evoked responses. This is a model paradigm for the analysis of high-frequency electroencephalography data at a very low signal-to-noise ratio, which emphasizes the differences of the explored methods. For the utilized data, it was found that the individual signal-to-noise ratio explained up to 74% of the performance differences between subjects. While data pre-processing was shown to increase average time-series classification performance, it could not fully compensate the signal-to-noise ratio differences between the subjects. This study proposes an algorithm to prototype and benchmark pre-processing pipelines for a paradigm and data set at hand. Extreme learning machines, Random Forest, and Logistic Regression can be used quickly to compare a set of potentially suitable pipelines. For subsequent classification, however, machine learning models were shown to provide better accuracy.

脑电图（electroencephalographic）数据的时间序列分类性能，在不同实验范式与研究受试者间存在显著差异。究其原因，除其他因素外，主要包括神经元处理过程的任务依赖性差异，以及受试者间看似随机的个体差异。现有研究对数据预处理技术改善此类挑战的效果探讨相对较少。本研究以高频躯体感觉诱发电位（high-frequency somatosensory evoked responses）为例，分析空间滤波器优化方法与非线性数据变换对时间序列分类性能的影响。该范式适用于极低信噪比（signal-to-noise ratio）场景下的高频脑电图数据分析，可有效凸显所探究方法间的性能差异。针对本次使用的数据集，研究发现个体信噪比可解释受试者间高达74%的性能差异。尽管数据预处理可提升时间序列分类的平均性能，但无法完全弥补受试者间的信噪比差异。本研究提出一种算法，可针对特定实验范式与所用数据集快速构建预处理流程原型并开展基准测试。可借助极限学习机（Extreme Learning Machines）、随机森林（Random Forest）与逻辑回归（Logistic Regression）快速比对一组潜在适配的预处理流程。但在后续分类任务中，针对性机器学习模型可实现更优的分类精度。