Introduction to Network Analysis on Time-Series of Brain Wave Data (EEG)
This article is part of the project “Identifying Mild Cognitive Impairment Using Network Analysis on Time-Series of Electroencephalogram (EEG),” a collaboration with BrainCodeCamp and BrainCode101.
Electroencephalogram (EEG) stands out as one of the most renowned methods for signal acquisition in neuroscience studies. While it has been in use for several decades, the application of Network Analysis methods isn’t as widespread as it may initially appear.
However, before delving into Network Analysis, it’s crucial to grasp the concept of a network. In mathematical terms, a network, also referred to as a graph, represents a structure that illustrates a collection of objects or individuals and the relationships between them. In the context of this problem, nodes represent EEG electrodes, while edges symbolize the connections or relationships between these nodes. In essence, it encapsulates the essence of an EEG signal network.
Creating a Network for EEG Signals
The process entails calculating the connectivity test for each pair of electrodes (signals) to determine the strength of the connection between that specific pair. This process is then repeated for every possible pair of EEG electrodes, leading to the emergence of the network.
Now that we have the network, it’s time for analysis.
To effectively analyze the network, we should first grasp what sets this network apart from others and how we can logically and mathematically measure these distinctions.
This image displays the public transportation network in ten European countries, with nodes representing hubs or stations and lines serving as connections. At first glance, it appears that both Belgium and Germany have the most robust transportation networks. But why is that?
Many people might initially consider counting the number of lines and comparing them between countries as the easiest solution. However, this may not be the most accurate measurement due to differences in terms of population, city locations, and other factors. Nevertheless, it can serve as a reasonably good proxy for assessing the quality of public transportation. It’s also closely related to a measurement called Network Density.
As the name implies, Network Density indicates how dense the network is. It is calculated using the following formula:
A denser network implies more connections, which can indicate a higher level of activity and interactions within the network. In the context of neuroscience, a denser network might suggest increased communication between brain regions. This can be valuable in understanding how specific brain functions or mechanics are operating and potentially identifying differences between healthy individuals and patients.
Applications to Brain Research
You can apply network analysis to EEG signals from healthy controls and patients. Have them perform tasks that activate the specific brain functions you’re interested in studying. After acquiring the EEG data, you can apply the network analysis method to assess the network’s properties and identify any differences between the two groups.
In essence, this approach allows you to study how the brain functions as a collective of interconnected regions, each responsible for specific tasks, and how these regions collaborate in different scenarios. Changes in network properties can indicate potential issues with brain mechanics or functioning, which can be a valuable tool in medical research and diagnosis.
I hope this explanation clarifies the concept of network analysis and its applications, particularly in the context of EEG signals and the study of brain mechanics. I hope you would be able to know which direction you should be heading with your data or project :)
