EEG Signal Analysis

Abstract

Epilepsy, a disorder that affects millions of people worldwide, often goes undiagnosed or misdiagnosed due to the unpredictable nature of seizures and the difficulty in monitoring brain activity over extended periods. Traditional methods of seizure detection, such as manual inspection of EEG signals by medical professionals, can be time-consuming and flat to errors. In contrast, this move towards utilizes EEG data, which captures the electrical activity of the brain, and applies CNNs to automatically detect seizures with high accuracy.

Introduction

Electroencephalography (EEG) is widely used in neuroscience and biomedical engineering for applications like brain-computer interfaces, sleep analysis, and seizure detection due to its high temporal resolution and non-invasiveness. Automating EEG signal classification, which involves artifact removal, feature extraction, and classification, is key to making EEG more practical and less dependent on experts. EEG data, structured as time-channel matrices of brain potentials, is well-suited for machine learning.

This review focuses on deep learning techniques, especially convolutional neural networks (CNNs) and hybrid models, for seizure detection using EEG. It discusses common datasets, performance metrics, and challenges in applying these methods.

Feature extraction reduces data dimensionality by creating informative, non-redundant features to improve learning and interpretation. Classification models are trained on labeled data to predict normal or abnormal brain states.

MATLAB is commonly used for EEG data processing and classification due to its powerful numerical and visualization tools. Performance metrics like accuracy, sensitivity, and specificity are used to evaluate classifiers' effectiveness in correctly identifying seizure events.

Conclusion

In conclusion, the use of Convolutional Neural Networks (CNNs) for epileptic seizure detection in MATLAB offers a highly effective and efficient approach to real-time monitoring of EEG signals. By leveraging the power of deep learning, the system can automatically identify subtle patterns in brain activity that distinguish normal conditions from seizure events, ensuring timely and accurate detection. This method not only reduces the burden on healthcare professionals but also has the potential to improve patient care by enabling faster interventions. With MATLAB\'s robust deep learning tools and its ability to process complex data efficiently, the proposed system represents a significant advancement in the field of medical diagnostics, providing a reliable, scalable solution for epilepsy monitoring and management.

References

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Copyright

Copyright © 2025 M. M. Kamble, Muskan I. Pathan, Jyoti R. Pujari, Afsana K. Tamboli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.