EEG signal pre-processing for segmentation into significants regions, major artefacts removal, and uncertainty reduction in epilectic seizure characterization

EEG analysis is a key tool in clinical research for diagnosing neurological disorders, but it presents challenges due to the low amplitude of the signals, the multichannel nature of recordings, and the frequent presence of artifacts and various events. These factors can compromise the accuracy of clinical interpretations. To address this, preprocessing methods are essential. They aim to remove major artifacts without losing valuable information, segment the EEG into meaningful epochs, and accurately characterize events such as epileptic seizures. The study focuses on using time-frequency analysis, particularly continuous wavelet transform, to divide the signal into significant regions for expert evaluation. It also explores various preprocessing techniques to reduce uncertainty in seizure detection, comparing their effectiveness in distinguishing seizure from non-seizure segments. These methods include wavelet and EMD-based approaches, polynomial modeling with channel correlation, phase space analysis, the use of the Navona descriptor, and source localization through dipole mapping. The results from each method can be combined to further enhance accuracy and reduce diagnostic uncertainty