ten Caat Michael, Maurits Natasha M, Roerdink Jos B T M
Department of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands.
IEEE Trans Vis Comput Graph. 2007 Jan-Feb;13(1):70-9. doi: 10.1109/TVCG.2007.9.
The field of visualization assists data interpretation in many areas, but does not manage all types of data equally well. This holds, in particular, for time-varying multichannel EEG data. No existing method can successfully visualize simultaneous information from all channels in use at all time steps. To address this problem, a new visualization method is presented based on the parallel coordinate method and making use of a tiled organization. This tiled organization employs a two-dimensional row-column representation, rather than a one-dimensional arrangement in columns as used for classical parallel coordinates. The usefulness of the new method, referred to as tiled parallel coordinates (TPC), is demonstrated by a particular type of EEG data. It can be applied to an arbitrary number of time steps, handling the maximum number of channels currently in use. An extensive user evaluation shows that, for a typical EEG assessment task, data evaluation by the TPC method is faster than by an existing clinical EEG visualization method, without loss of information. The generality of the TPC method makes it widely applicable to other time-varying multivariate data types.
可视化领域在许多领域辅助数据解释,但并非对所有类型的数据都能同等出色地进行处理。对于随时间变化的多通道脑电图(EEG)数据而言尤其如此。现有的方法都无法在所有时间步成功可视化所有使用通道的同步信息。为解决这一问题,本文提出了一种基于平行坐标法并采用平铺式结构的新可视化方法。这种平铺式结构采用二维行 - 列表示,而非经典平行坐标中使用的一维列排列。通过一种特定类型的EEG数据证明了这种被称为平铺平行坐标(TPC)的新方法的有效性。它可以应用于任意数量的时间步,处理当前使用的最大通道数。广泛的用户评估表明,对于典型的EEG评估任务,使用TPC方法进行数据评估比现有临床EEG可视化方法更快,且不会丢失信息。TPC方法的通用性使其广泛适用于其他随时间变化的多变量数据类型。