EEG SEIZURE DETECTION AND ANALYSIS USING MULTI-DIMENSIONAL FEATURE EXTRACTION AND PCA
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Abstract
Electroencephalography (EEG) is crucial for diagnosing epilepsy by revealing brain electrical patterns. However, various artifacts like eye movements and muscle contractions can distort EEG data, making it difficult to detect seizures accurately. This study proposes an effective methodology to improve seizure detection and analysis by employing advanced feature extraction and principal component analysis (PCA) to reduce artifact impact, ultimately enhancing EEG interpretation and patient outcomes.
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