Electroencephalography (EEG) signals are intrinsically contaminated by a variety of non-neural artifacts, which strongly degrade signal quality and hinder the extraction of meaningful neural information. Advanced artifact removal is usually performed offline and requires expert supervision, limiting its applicability in scenarios that demand rapid or real-time evaluation. This thesis proposes and evaluates a cleaning pipeline designed for near real-time EEG processing, aiming to provide artifact-reduced signals within a latency below two minutes. The approach combines Independent Component Analysis (ICA) and Artifact Subspace Reconstruction (ASR), integrated with ICLabel to support automatic and expert-like component classification. The pipeline was validated offline by simulating real-time streaming conditions on the TUAR annotated dataset, enabling quantitative comparison against ground-truth artifact labels.