Spatiotemporal analysis of multichannel EEG: CARTOOL

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biomedical Engineering

Background:

• Multichannel Electroencephalogram (EEG) records brain's electric fields.
• Analyzing spatial properties and temporal changes in EEG topographies is crucial for understanding brain function.
• Existing methods may lack reference independence and statistical clarity.

Purpose of the Study:

• To describe methods for analyzing brain electric fields using multichannel EEG.
• To demonstrate the implementation of these methods in the CARTOOL software.
• To enable quantitative assessment of changes in EEG field topographies.

Main Methods:

• Reference-independent topographic analysis of EEG data.
• Global measures of field strength and similarity.
• Temporal segmentation based on topographic variations.
• Frequency domain and statistical analysis of topographies.
• Source imaging using distributed inverse solutions.
• Implementation within the CARTOOL software package.

Main Results:

• CARTOOL provides a comprehensive suite of tools for EEG analysis.
• The software facilitates the visualization of data and analysis results using 3D displays.
• Analysis methods are reference-independent, yielding statistically unambiguous results.
• Topographic differences directly reflect changes in neuronal source configurations.

Conclusions:

• CARTOOL is a valuable, freely available tool for researchers and clinicians.
• It enhances the interpretation of multichannel EEG and evoked potentials.
• The software promotes a global, comprehensive, and unambiguous understanding of brain activity.