Brainwaves glowing inside a head silhouette, representing MS diagnosis.

Brain Signals Unlock Clues to Multiple Sclerosis: A New Era of Diagnosis?

Soraya Malik in Health & Wellness June 2025 • 4 min read.

"Cutting-edge research explores how EEG and AI could revolutionize the detection and treatment of MS, offering hope for earlier and more accurate diagnoses."

Multiple sclerosis (MS) is a complex and often debilitating neurological disease that affects millions worldwide. Early and accurate diagnosis is crucial for managing the condition, slowing its progression, and improving the quality of life for those affected. However, diagnosing MS can be challenging, often involving a combination of clinical evaluations, imaging techniques, and laboratory tests. This process can be time-consuming and may not always provide a clear-cut answer, especially in the early stages of the disease.

Now, imagine a future where MS could be detected simply and accurately by analyzing your brainwaves. Recent research is paving the way for just such a breakthrough, using electroencephalography (EEG) and sophisticated machine-learning algorithms to identify patterns in brain activity that are indicative of MS. This innovative approach holds the potential to revolutionize MS diagnosis, enabling earlier interventions and more personalized treatment strategies.

This article explores the exciting possibilities of using brain signals and artificial intelligence to detect MS. We'll delve into the details of a recent study that demonstrates the effectiveness of this method, its potential benefits, and what it could mean for the future of MS diagnosis and treatment.

Decoding Brain Signals: How EEG and AI Detect MS

Brainwaves glowing inside a head silhouette, representing MS diagnosis.

The study, led by researchers Amirmasoud Ahmadi, Saeideh Davoudi, and Mohammad Reza Daliri, introduces a new Computer-Aided Diagnosis (CAD) system that utilizes EEG signals to detect MS. This system focuses on phase-amplitude coupling (PAC), a measure of how different brainwave frequencies interact with each other, during covert visual attention tasks. Covert visual attention refers to the ability to focus on something without overtly moving your eyes or head.

Here’s how the process works:

EEG Recording: Participants wear an EEG headset that records their brain activity while performing specific visual attention tasks.
Signal Processing: The EEG data is then processed to remove noise and artifacts, isolating the relevant brainwave frequencies.
Feature Extraction: The system calculates PAC values between different brainwave frequencies and electrodes on the scalp. These PAC values serve as unique 'features' that characterize the brain's activity.
Machine Learning: A machine-learning algorithm is trained to identify patterns in these PAC features that distinguish between individuals with MS and healthy controls.

The researchers used an online sequential extreme learning machine (OS-ELM) classifier, combined with a T-test feature selection method, to achieve impressive accuracy. The system reached 91% accuracy in identifying MS during color-related tasks and 90% accuracy during direction-related tasks. This high level of accuracy suggests that the system can effectively detect subtle changes in brain activity associated with MS.

A Promising Future for MS Diagnosis and Treatment

This research offers a promising new avenue for the early and accurate diagnosis of MS. By combining EEG technology with machine learning, this system can potentially detect subtle changes in brain activity that may not be apparent through traditional diagnostic methods. Early detection can lead to earlier intervention, which can help slow the progression of the disease and improve the quality of life for individuals with MS. While further research and validation are needed, this innovative approach represents a significant step forward in the fight against MS.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.cmpb.2018.11.006, Alternate LINK

Title: Computer Aided Diagnosis System For Multiple Sclerosis Disease Based On Phase To Amplitude Coupling In Covert Visual Attention

Subject: Health Informatics

Journal: Computer Methods and Programs in Biomedicine

Publisher: Elsevier BV

Authors: Amirmasoud Ahmadi, Saeideh Davoudi, Mohammad Reza Daliri

Published: 2019-02-01

Everything You Need To Know

What is the main goal of the study using EEG and AI for MS detection?

The main goal of the study is to develop a method for earlier and more accurate detection of Multiple Sclerosis (MS) using electroencephalography (EEG) and machine-learning algorithms. This aims to improve the management of the condition, slow its progression, and enhance the quality of life for those affected by MS. The study leverages EEG signals and a Computer-Aided Diagnosis (CAD) system to identify patterns indicative of MS.

How does the Computer-Aided Diagnosis (CAD) system, utilizing EEG, work to detect MS?

The CAD system uses electroencephalography (EEG) to record brain activity, specifically focusing on phase-amplitude coupling (PAC). Participants perform covert visual attention tasks while wearing an EEG headset. The system processes the EEG data, removes noise, and extracts relevant brainwave frequencies. It then calculates PAC values, which serve as unique features. A machine-learning algorithm, such as the online sequential extreme learning machine (OS-ELM), is trained to identify patterns in these PAC features that distinguish between individuals with MS and healthy controls. This approach allows for the identification of subtle changes in brain activity associated with MS.

What are the roles of EEG, PAC, and OS-ELM in detecting Multiple Sclerosis (MS)?

In this innovative approach, EEG is used to record brain signals during specific visual attention tasks. Phase-amplitude coupling (PAC) is then analyzed, measuring how different brainwave frequencies interact with each other. These PAC values are extracted as features. The online sequential extreme learning machine (OS-ELM), a machine-learning algorithm, is used to identify patterns in these PAC features. The OS-ELM classifier, combined with a T-test feature selection method, helps distinguish between individuals with MS and healthy controls, leading to accurate detection.

What is the significance of the study's accuracy rates (91% and 90%) in detecting MS?

The high accuracy rates of 91% during color-related tasks and 90% during direction-related tasks are very significant. These figures indicate that the system can effectively detect subtle changes in brain activity associated with MS. This level of accuracy suggests that the combined approach of EEG and machine-learning can be a reliable method for identifying MS, potentially allowing for earlier intervention and more personalized treatment strategies. High accuracy is critical for clinical applications, reducing the chances of misdiagnosis and improving patient outcomes.

Beyond diagnosis, how could this EEG and AI approach transform the treatment of Multiple Sclerosis (MS)?

The EEG and AI approach could transform MS treatment by enabling earlier interventions and personalized treatment strategies. Early detection allows for timely initiation of disease-modifying therapies, which can slow the progression of the disease and reduce the severity of symptoms. This approach also opens the door to more personalized treatment plans tailored to the individual's specific disease characteristics, as identified through their brain activity patterns. In the future, this method may also help monitor treatment effectiveness and adjust therapies accordingly, thus improving the overall quality of life for individuals with MS.