Design and Evaluation of Tiled Parallel Coordinate Visualization of Multichannel EEG Data

Caat, M. ten; Maurits, Natasha M.; Roerdink, Jos B. T. M.

doi:10.1109/tvcg.2007.9

Cited by 24 publications

(19 citation statements)

References 19 publications

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“…end for 11: end for 12: for i = 1 to |marker| do 13: for j = 1 to |marker| do 14: bsnMat(i, j) ← 1 {IWB modification} 15: end for 16: end for MAIN: 17: while queue = ∅ do 18: e(v, v ′ ) ← dequeue(queue) 19:…”

Section: Fu Visualization a Fu Map For Individual Dataset Analysismentioning

confidence: 99%

“…The value s for an electrode is mapped to the gray value of its corresponding Voronoi cell, with lighter gray for higher average FU sizes, similar to a (gray scale) topographic map [19]. Consequently, a light Voronoi cell indicates that the corresponding electrode is on average part of large FUs.…”

Section: ) Group Mean Coherence Mapmentioning

confidence: 99%

“…Visualization of multichannel EEG (at least 64 electrodes) is not always managed well [18]- [20]. Researchers often employ a hypothesis-driven definition of certain ROIs in which all electrodes are assumed to record similar signals because of volume conduction effects [21].…”

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confidence: 99%

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Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Caat

Maurits

Roerdink

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit (FU) as a data-driven region of interest (ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier we presented two methods to detect FUs, a maximal clique based (MCB) method (time complexity O(3 n/3 ), with n the number of vertices) and a more efficient watershed based (WB) method (time complexity O(n 2 log n)). To reduce the potential over-segmentation of the WB method, we introduce an improved watershed based (IWB) method (time complexity O(n 2 log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB method both are up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that, considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data-driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis. Results, in accordance with conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis-driven ROIs could be extended with additional datadriven ROIs.

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Section: Fu Visualization a Fu Map For Individual Dataset Analysismentioning

confidence: 99%

Section: ) Group Mean Coherence Mapmentioning

confidence: 99%

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Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Caat

Maurits

Roerdink

2008

IEEE Trans. Visual. Comput. Graphics

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“…In this work, we enhance some of their techniques with interactive visualization and exploration of both the spectral dynamics as well as the spatial characteristics associated with EEG. Our system is a step to extend more traditional spatio-temporal ERP analysis techniques [43] with additional information regarding the frequency content of EEG signals.…”

Section: Related Workmentioning

confidence: 99%

Discovering and visualizing patterns in EEG data

Anderson

Chong

Preston

et al. 2013

2013 IEEE Pacific Visualization Symposium (PacificVis)

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Figure 1: Our system is composed of many linked views. Structural MRI data is presented in a 3D visualization (top left) whose transfer function is manipulated using 2D widgets (bottom right). Users may select individual EEG sensors (yellow sphere) in the 3D visualization to explore its raw signal (bottom left) and time-frequency representation (middle right). The user also interacts with the time-frequency query area (top right) to generate spectral queries. The time-frequency display and query area are colormapped using an interactive widget (middle left). Combined with the settings in the parameter dialog, the query is issued and relevant sensors are colored blue according to their correlation with the query. ABSTRACTBrain activity data is often collected through the use of electroencephalography (EEG). In this data acquisition modality, the electric fields generated by neurons are measured at the scalp. Although this technology is capable of measuring activity from a group of neurons, recent efforts provide evidence that these small neuronal collections communicate with other, distant assemblies in the brain's cortex. These collaborative neural assemblies are often found by examining the EEG record to find shared activity patterns.In this paper, we present a system that focuses on extracting and visualizing potential neural activity patterns directly from EEG data. Using our system, neuroscientists may investigate the spectral dynamics of signals generated by individual electrodes or groups of sensors. Additionally, users may interactively generate queries which are processed to reveal which areas of the brain may exhibit common activation patterns across time and frequency. The utility of this system is highlighted in a case study in which it is used to analyze EEG data collected during a working memory experiment. *

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“…Ten Caat et al focus on a clinical application scenario in which temporal EEG data is explored by students, researchers and experts to assess latencies, amplitudes and symmetries [3]. Their work is a good example of how PCPs can be successfully adapted to improve the assessment of complex medical data.…”

Section: Related Workmentioning

confidence: 99%

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

Blaas

Botha

Post

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.

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Design and Evaluation of Tiled Parallel Coordinate Visualization of Multichannel EEG Data

Cited by 24 publications

References 19 publications

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

Discovering and visualizing patterns in EEG data

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

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