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The implementation of the learning model is primarily dependent on the features extracted from the EEG signals for any mental task classification model. A feature depicts an identifiable measurement, ...

EEG data were collected between two conditions, relax wakefulness (close-eyes) and non-relax (IQ test). Data segmentation and linear regression model is used to extract the EEG features and to obtain ...

Each patient finally had 1,200s of data for each of EEG, ECG, and fNIRS, and thus the segmentation created 300 epochs per signal per patient. Finally, each data array was normalized by subtracting the ...

Compared to direct feature extraction from complex EEG data for classification, converting PD into multimodal features offers extremely high interpretability and visualization. To summarize the three ...

This project uses machine learning algorithms to analyze EEG signals and identify patterns and abnormalities for improved diagnosis and treatment of neurological disorders. It involves pre-processing ...