Advanced Digital Filtering and Machine Learning–Based EEG Signal Analysis for Optimized Brain–Computer Interface Applications

Electroencephalogram (EEG) signals are widely used in Brain–Computer Interface (BCI) systems for enabling direct interaction between the human brain and external devices. However, EEG signals are low in amplitude and highly susceptible to noise, making accurate extraction of meaningful features challenging. This paper proposes an integrated framework combining advanced digital filtering techniques and machine learning approaches to improve EEG signal quality and classification accuracy for optimized BCI performance. A comparative study of Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filters is presented, along with a custom-designed Digital Signal Processing (DSP) pipeline. Principal Component Analysis (PCA) coupled with Feature Transformation methods is used for dimensionality reduction and noise suppression. Classification is performed using Support Vector Machines (SVM) and Random Forest models. Experimental results on benchmark EEG datasets indicate significant improvement over baseline approaches, demonstrating robustness and real-time feasibility for BCI applications.