Cognitive phenomena measurement with time window-based multispectral brain mapping

Area of Science:

• Cognitive Neuroscience
• Neuroimaging
• Signal Processing

Background:

• Accurate measurement and analysis are crucial for cognitive neuroscience experiments.
• Electroencephalography (EEG) is a primary tool for monitoring brain activity.
• Innovations are needed to extract more comprehensive information from EEG signals.

Purpose of the Study:

• To present a new tool for measuring and mapping cognitive phenomena.
• Utilizes time-window-based multispectral brain mapping of EEG signals.

Main Methods:

• Developed in Python, the tool generates brain maps for six EEG spectra (Delta, Theta, Alpha, Beta, Gamma, Mu).
• Accepts arbitrary EEG channels (10-20 system) and allows user selection of channels, frequency bandwidth, signal processing, and time window length.
• Enables short-time brain mapping for detailed cognitive phenomenon exploration.

Main Results:

• The tool was tested on real EEG signals, demonstrating effective and accurate mapping of cognitive phenomena.
• Validated its capability for precise measurement and visualization of brain activity.

Conclusions:

• The developed tool offers a valuable method for cognitive neuroscience research and clinical studies.
• Future work will focus on performance optimization and feature expansion.