Artificial intelligence for brain disease diagnosis using electroencephalogram signals.

Area of Science:

• Neuroscience and Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• Brain signals, including electrical activity measured by electroencephalogram (EEG), provide insights into brain function.
• Deviations in EEG patterns indicate abnormal brain activity linked to neurological diseases.
• Brain-computer interface (BCI) systems translate brain signals for external device interaction.

Purpose of the Study:

• To review the application of artificial intelligence (AI) in diagnosing brain diseases using EEG data.
• To highlight advancements in AI algorithms for EEG-based brain disease detection and prediction.

Main Methods:

• Investigated the integration of AI techniques, specifically machine learning (ML) and deep learning (DL) models, with EEG data.
• Analyzed AI's role in enhancing the precision and accuracy of BCI systems for neurological disease diagnosis.

Main Results:

• AI, ML, and DL models show significant success in classifying and predicting various brain diseases from EEG signals.
• AI integration has demonstrably improved the performance and capabilities of BCI technology in this domain.

Conclusions:

• AI is a powerful tool for advancing EEG-based brain disease diagnosis and BCI applications.
• Continued research in AI algorithms promises further improvements in understanding and managing neurological conditions.