Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models

Electroencephalography (EEG) is a valuable tool for studying brain function and identifying neurological disorders. This study aimed to analyze EEG data using various techniques for feature extraction and classification. The data was preprocessed by applying filters and dividing it into epochs. Feature extraction techniques, including Fast Fourier Transform (FFT) in the frequency domain and Continuous Wavelet Transform (CWT) in the time-frequency domain, were applied to convert the EEG signals into scalograms and spectrograms. The primary objective was to classify individuals with Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using the scalograms and spectrograms with 2D Convolutional Neural Networks (CNN) and 2D Convolutional Recurrent Neural Networks (CRNN). The classification results obtained from epochs of different durations (5 seconds and 2 seconds) were compared. The analysis revealed that the 2D CRNN model incorporating scalograms achieved the highest classification accuracy of 87.79% for 5 sec epochs and 88.25% for 2 sec epochs. This demonstrates the effectiveness of using scalograms and spectrograms in combination with deep learning models for accurately classifying individuals with MCI and HC with EEG data.