Few-channel electroencephalogram （EEG） is more suitable and affordable for practical use as a portable or wearable device， but it is subject to a variety of unpredictable artifacts， making removal of artifacts extremely difficult. In the feature space， the artifact-related components are dispersed while the components related to brain activities are closely distributed. We propose an outlier detection-based method for artifact removal under the few-channel condition. The underlying components （sources） are extracted using wavelet decomposition and blind source separation methods， and the artifact-related components far from the center of distribution of all components are considered as outliers and are identified using one-class support vector machine. In the quantitative analyses with semi-simulated data， the proposed method outperforms the threshold-based methods for various artifacts， including EMG， electro-oculogram（EOG） and power line noise. The visualization of the clusters of components demonstrates the effectiveness of the hypothesis. This study innovatively combines the ideas of blind source separation and outlier detection， without setting artifact-specific parameters， and is capable of adaptively removing various artifacts while effectively retaining brain activities， showing excellent performance and usability.