Electroencephalogram (EEG), measures the neural activity of the central nervous system, which is widely used in diagnosing brain activity and therefore plays a vital role in clinical and Brain-Computer Interface application. However, analysis of EEG signal is often complex since the signal recoding often contaminates with noises or artifacts such as ocular and muscle artifacts, which could mislead the diagnosis result. Therefore, to identify the artifacts from the EEG signal and handle it in a proper way is becoming an important and interesting research area. This paper presents an automated EEG artifacts handling approach, where it combines Independent Component Analysis (ICA) with a 2^nd order clustering approach. Here, the 2^nd order clustering approach combines the Hierarchical and Gaussian Picture Model clustering algorithm. The effectiveness of the proposed approach has been examined and observed on the real EEG recording. According to the result, artifacts in the EEG signals are identified and removed successfully where the artifacts handled EEG signals show acceptable considering visual inspection.