EEG Biomarkers: The Future of Monitoring Neurological Health?

Neurodegenerative conditions, such as Alzheimer’s disease, are very complex and typically develop over many years. A variety of changes take place in the brain, and not all of these changes happen at the same time. A single test or scan may not be able to capture this variety of processes, and it may not be able to accurately reflect the evolution of the disease across all its stages.

Having a variety of biomarkers provides more options for clinical experts to understand what is going on in the disease process. Biomarkers capture different phenomena of the evolution of the disease. For example, an imaging scan may show whether some regions of the brain have shrunk or whether there is a localized lesion. A spinal fluid test may pick up a buildup of abnormal proteins in the brain. These processes happen at different stages in the disease. Having diverse biomarkers may also help to distinguish different types of neurological conditions. The ultimate aim is to diagnose these conditions earlier so that, hopefully, disease-modifying drugs can slow disease progression.

An EEG is a test that measures the small electric fields generated by neurons, the brain’s nerve cells. Each neuron produces a tiny electrical current when it becomes active. When enough nearby neurons become active together, the electric field becomes large enough so that it can be detected non-invasively, over the scalp, with appropriate electrodes on the skin.

The voltages captured by these electrodes are then magnified and recorded by a computer. It is typical to place 20 or so electrodes on the scalp to capture fields generated by different brain regions, but, depending on the application, this number of electrodes could be lower or much higher. In this way, the patterns captured in the EEG can tell us a lot about how well the brain functions.

It is common to use the EEG in clinical practice to diagnose and monitor conditions such as epilepsy. Though the EEG is not yet accepted as a biomarker for dementia, or Alzheimer’s disease in particular, there is increasing interest in this application. There is evidence that Alzheimer’s disease disrupts how different brain regions communicate with each other. There is also increasing interest in enabling the use of EEG as a remote screening and/or monitoring tool. The EEG is non-invasive, affordable in comparison with other neuroimaging techniques and portable.

This makes the EEG an ideal candidate to assess brain activity remotely, away from hospitals. This could be valuable, for example, to monitor epilepsy at home or to assess brain damage after an accident.

Yes. A notorious example is epilepsy, where EEG is used in clinical practice to determine the presence and type of abnormal electrical bursts in the brain that lead to seizures.

In the past, the field of EEG analysis has been held back by a lack of large datasets and standardization procedures to ensure that EEG data acquired at different labs can be analyzed together. However, this is changing rapidly.

Several research groups and consortia are now sharing EEG data. This facilitates research and helps ensure that the findings can be generalized to different settings, importantly low- and middle-income countries. Progress is being made, but there is still work to be done. Beyond the availability of data itself, it is important to ensure that the analysis of EEG that would lead to a potential biomarker is reliable and not influenced by external factors that may vary from day to day. It is also important to ensure that any potential biomarker is not only sensitive enough to pick up that something abnormal is going on in the brain, but it is also specific enough to inform about what the cause of the abnormality may be.

I envisage “multimodal” biomarkers to inform clinicians about the “brain health” of a person. These multimodal biomarkers would combine, with the help of artificial intelligence, information obtained from neuroimaging, blood tests, EEG and other methods with the personal clinical history of the individual. In this way, they would assist clinicians in achieving patient-specific early diagnosis of neurological conditions, such as Alzheimer’s disease.

I think that the EEG has an important role to play in this future, given its non-invasiveness and affordability, which enables the possibility to acquire EEG data at home or in the community, even in combination with data about the performance of the person on certain tasks (such as games on tablets or phones). The wealth of longitudinal data acquired in this way would enable us to detect promptly very subtle changes in brain function caused by neurological diseases, even before serious symptoms arise.

References:

1. Steinmetz JD, Seeher KM, Schiess N, et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 2024;23(4):344-381. doi: 10.1016/S1474-4422(24)00038-3

2. Al-Qazzaz NK, Ali SHBMD, Ahmad SA, Chellappan K, Islam MdS, Escudero J. Role of EEG as biomarker in the early detection and classification of dementia. Sci World 2014;2014(1):906038. doi: 10.1155/2014/906038

3. Rossini PM, Di Iorio R, Vecchio F, et al. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophys. 2020;131(6):1287-1310. doi: 10.1016/j.clinph.2020.03.003

4. Babiloni C, Arakaki X, Baez S, et al. Alpha rhythm and Alzheimer’s disease: Has Hans Berger’s dream come true? Clin Neurophys. 2025;172:33-50. doi: 10.1016/j.clinph.2025.02.256