Preprocessing EEG Signals with Signal Filtering, Downsampling, and Noise Removal Techniques

Before analyzing EEG signals, preprocessing is essential to prepare the data. Preprocessing includes signal filtering, downsampling, and noise removal steps. In the signal filtering step, digital filters are applied to the raw signals to eliminate unwanted high-frequency and low-frequency noise. This can be implemented using bandpass or notch filters in MATLAB with functions like `filtfilt` for zero-phase filtering to prevent phase distortion. In the downsampling step, the signal's sampling rate is reduced to decrease data volume and improve computational efficiency, which can be achieved using the `resample` function to avoid aliasing effects. Finally, in the noise removal step, algorithms with high signal-to-noise ratios, such as Independent Component Analysis (ICA) or wavelet denoising, are employed to eliminate noise from the signals, facilitating more accurate analysis. For instance, ICA can be implemented with the `fastica` function to separate neural activity from artifactual components.