The Role of Electroencephalography in Epilepsy Research: From Seizures to Interictal Activity and Comorbidities

Academic Background

Since the early 20th century, electroencephalography (EEG) has been a key tool in epilepsy research. EEG technology not only helps scientists record and analyze brain signals but also reveals the pathophysiological mechanisms, functional network organization, and mechanisms underlying seizures. With technological advancements, the application of EEG has expanded to include ultra-slow and high-frequency activities, providing new perspectives on understanding the onset and development of epilepsy. However, EEG technology still faces challenges such as limited spatial resolution and artifact interference. Therefore, researchers continue to explore ways to improve EEG technology and integrate it with modern techniques like optogenetics, chemogenetics, and imaging to gain a more comprehensive understanding of epilepsy.

Paper Source

This article is co-authored by Christos Panagiotis Lisgaras, Liset M. de la Prida, Edward Bertram, and other experts from renowned international institutions such as New York University Grossman School of Medicine, Instituto Cajal-CSIC (Spain), University of Virginia, among others. It was published in the journal Epilepsia in 2025 under the title “The Role of Electroencephalography in Epilepsy Research—from Seizures to Interictal Activity and Comorbidities.”

Paper Themes and Main Points

Main Authors and Their Backgrounds

This paper is co-authored by experts across multiple fields including neuroscience, psychiatry, and pharmacology. Key authors include Christos Panagiotis Lisgaras (Department of Psychiatry, New York University Grossman School of Medicine), Liset M. de la Prida (Instituto Cajal, CSIC, Spain), Edward Bertram (University of Virginia), and others. These authors have extensive experience and significant influence in epilepsy research.

Main Content of the Paper

Historical Significance and Future Trends of EEG

The paper first reviews the historical importance and significance of EEG in epilepsy research. Since the late 19th century, EEG technology has evolved from simple brain wave recordings to complex multi-channel microelectrode array recordings. With advancements in computer technology and data analysis algorithms, EEG has become a powerful tool capable of capturing brain activity at various temporal and spatial scales, thus revealing the complex mechanisms of epilepsy.

Traditional Methods and New Technologies in EEG

Traditional EEG recordings typically focus on Berger frequency ranges, while experimental studies have expanded to wider frequency bands, including ultra-slow waves and high-frequency oscillations (HFOs). Novel electrode designs (such as multichannel recording, injectable electrodes, dissolvable electrodes, and foldable electrodes) enable long-term chronic recordings. Moreover, integrating EEG with advanced technologies like optogenetics, chemogenetics, and imaging further enhances the understanding of epileptic network dynamics.

Applications of EEG in Epilepsy-Related Cognitive Deficits and Other Comorbidities

Beyond detecting seizures and interictal activity, EEG plays a crucial role in elucidating epilepsy-related cognitive deficits and other comorbidities. Studies show that interictal epileptiform discharges (IEDs) can impact memory and cognitive functions, particularly during non-rapid eye movement sleep. The frequency of IEDs correlates closely with the degree of neuropsychological impairment, providing important clues for understanding cognitive disorders in epilepsy patients.

Challenges and Solutions

Despite significant advancements, EEG technology still faces challenges. For example, EEG has limited spatial resolution and is prone to volume conduction effects; animal EEG requires invasive electrode implantation; long-term continuous recording is challenging for developing animals; artifacts and EEG interpretation remain persistent issues. To address these challenges, researchers have developed advanced signal processing techniques and machine learning algorithms to improve the quality and accuracy of EEG data.

Supporting Evidence and Theoretical Basis

Development History of EEG Technology

The development of EEG technology began in the late 19th century, initially limited to simple brain wave recordings. Advances in amplifier design and digitization gradually made EEG an important research tool. Modern EEG technology can record not only traditional Berger frequency range signals but also ultra-slow waves and high-frequency activities. These technological advancements provide new perspectives on studying the onset and development of epilepsy.

Novel Electrode Designs and Multimodal Recording

Novel electrode designs (such as multichannel recording, injectable electrodes, dissolvable electrodes, and foldable electrodes) enable long-term chronic recordings. Simultaneously, integrating EEG with advanced technologies like optogenetics, chemogenetics, and imaging further enhances the understanding of epileptic network dynamics. For instance, optogenetics can be used for cell-type-specific control of neuronal activity, revealing the roles of specific cell types in epileptic network activity.

Application of EEG in Cognitive Function

Studies show that interictal epileptiform discharges (IEDs) can affect memory and cognitive functions, particularly during non-rapid eye movement sleep. The frequency of IEDs is closely related to the degree of neuropsychological impairment. For example, IEDs in children are associated with deficits in processing speed, attention, visuospatial function, and arithmetic ability. Additionally, the frequency of IEDs can predict performance on general intelligence tests and academic achievements. These findings provide important clues for understanding cognitive impairments in epilepsy patients.

Advanced Signal Processing and Machine Learning

To address challenges in EEG recording, researchers have developed various advanced signal processing techniques and machine learning algorithms. For example, independent component analysis (ICA) can remove artifacts, and convolutional neural networks (CNNs) can automatically detect EEG patterns. These techniques enhance the quality and accuracy of EEG data, aiding in a better understanding of the complex mechanisms of epilepsy.

Significance and Value of the Paper

Scientific Value

This paper systematically reviews the application of EEG in epilepsy research, revealing the development of EEG technology over the past century and its impact on future research. By combining traditional EEG technology with modern advanced technologies, researchers can gain a more comprehensive understanding of the complex mechanisms of epilepsy, providing important references for future treatment strategies.

Application Value

EEG technology not only plays a significant role in basic research but also has broad applications in clinical diagnosis and treatment. For example, EEG can help doctors accurately locate epileptic foci, evaluate the effectiveness of anti-epileptic drugs, and even predict the likelihood of seizures. Furthermore, integrating EEG with other advanced technologies offers new possibilities for personalized treatment.

Innovation

This paper presents several innovative viewpoints and technologies. For instance, researchers have developed various novel electrode designs and multimodal recording methods, enabling long-term chronic recordings. Integrating EEG with advanced technologies like optogenetics, chemogenetics, and imaging provides new tools for studying epileptic network dynamics. Additionally, the application of advanced signal processing and machine learning algorithms significantly improves the quality and accuracy of EEG data.

Conclusion

EEG technology has made significant progress over the past century and has become an indispensable tool in epilepsy research. By combining traditional EEG technology with modern advanced technologies, researchers can gain a more comprehensive understanding of the complex mechanisms of epilepsy, providing important references for future treatment strategies. Despite ongoing challenges, EEG will continue to play a critical role in epilepsy research as technology continues to advance.