<p>EEG signal processing is the process of analysing the raw electrical signals recorded by the electrodes on the scalp to obtain information about the human brain. The preprocessing of the EEG signal cleans the raw signals, feature extraction to obtain the relevant patterns, and classification to study the different states of the brain. The study helps in the identification of brain diseases, diagnostic conditions, and neurological disorders such as strokes and epilepsy. The EEG signals obtained from the channel electrodes are affected due to the electrical source noise, eye blinking, and muscle activity. The selection of the appropriate filter is very important in the processing of EEG signals, and eliminating the source noise in the raw signal to improve the overall signal-to-noise ratio for the effective analysis and critical study of the disease. The review article focuses on the study of the different filters used in the EEG-based system, applied in the analysis of epileptic seizures with feature extraction and classification. EEG signal processing is done based on appropriate filtering to suppress artifacts, and commencing with consistent data acquisition to improve signal quality. The actual features are then extracted and refined before accessing the different classification algorithms. The system helps in ensuring the accurate distinction between seizure and non-seizure identifications for effective neurological assessment. The article reviews several EEG-signal-processing methods, identifies key gaps in the present research, and proposes promising avenues for future investigation. The competent EEG signal processing depends on well-designed FIR and IIR filters to suppress noise and preserve critical neural features, supporting more accurate classification of epileptic signals.</p>