Advanced EEG Analysis Techniques for Neurological Disorders Vol. II

Given the success of the Advanced EEG Analysis Techniques for Neurological Disorders and the rapidly evolving subject area, we are pleased to announce the launch of Volume II.

Electroencephalography (EEG) is a versatile tool that has significantly impacted the understanding and treatment of various neurological disorders. Due to its non-invasive nature and high temporal resolution, it allows the monitoring of brain activity in both real-time and across extended periods. Recent advancements in EEG technology, coupled with sophisticated computational algorithms, have provided unprecedented insights into the brain's functioning. However, the full potential of EEG analysis in detecting, diagnosing, and treating neurological disorders remains underexplored.



The primary goal of this Research Topic is to highlight and address recent developments in EEG analysis techniques for neurological disorders. While traditional methods have been pivotal in understanding neurological disorders, leading researchers are innovating advanced EEG analysis techniques leveraging artificial intelligence, machine learning, and deep learning. These technologies are revolutionizing our ability to understand and treat disorders including epilepsy, Alzheimer's disease, depression, encephalopathy, and others.



This Research Topic aims to gather researchers working on advanced EEG analysis techniques to share their knowledge, methodologies, and applications in a comprehensive manner. The ultimate aim is to expedite the evolution of EEG-based diagnosis and treatment strategies augmenting the fight against neurological disorders effectively.



This Research Topic welcomes original research articles, reviews, method articles, and clinical trial papers that cover but are not limited to the following themes:



-Advanced signal processing techniques in EEG analysis

-Application of machine learning, deep learning, and other AI techniques in EEG analysis

-Novel methodologies for identifying neurological disorders using EEG

-Enhanced understanding of neurological disorders through EEG analysis

-Recent advancements in real-time EEG monitoring

-Innovative predictive models for neurological disorders based on EEG data



The emphasis is on showcasing EEG analysis methodologies that provide enhanced predictive accuracy, increased computational efficiency, and improved understanding of underlying neurological ailments. We welcome Original Research, Reviews, Methodology, Clinical Trials, Perspectives, and Short Communications articles. These contributions should ultimately pave the way for innovative therapeutic and diagnostic strategies for neurological disorders.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Electroencephalography (EEG), EEG Analysis, Machine Learning, Neurology, Predictive Models, Neurological Disorders

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.