An Optimized Technique for Feature Extraction and EEG Signal Processing using MATLAB

Brain-Computer Interfaces (BCI), cognitive research, and medical diagnosis all make extensive use of electroencephalography (EEG) to analyze brain activity. However, issues with EEG signal processing include limited classification accuracy, ineffective feature extraction, and noise interference. With an emphasis on enhanced feature extraction and classification, this study uses MATLAB to optimize EEG signal processing. Enhanced preprocessing methods, Independent Component Analysis (ICA) for artifact removal, and Butterworth and Chebyshev filters for noise reduction are all part of the optimization process. Time-domain, frequency-domain, and time-frequency representations like Wavelet Transform and Power Spectral Density (PSD) are used to enhance feature extraction. Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are used for classification, which increases accuracy. Cross-validation and statistical analysis are used to verify the precision and accuracy of the results. A comparative analysis reveals advancements over current practices. In addition to discussing improvements in preprocessing clarity, the paper presents methods to improve cross-correlation (CC) and signal-to-noise ratio (SNR). Future research will integrate deep learning with real-time EEG streaming.