An electroencephalogram (EEG) is the most extensively used physiological signal in emotion recognition using biometric data. However, these EEG data are difficult to analyze, because of their anomalous characteristic where statistical elements vary according to time as well as spatial-temporal correlations. Therefore, new methods that can clearly distinguish emotional states in EEG data are required. In this paper, we propose a new emotion recognition method, named AsEmo. The proposed method extracts effective features boosting classification performance on various emotional states from multi-class EEG data. AsEmo Automatically determines the number of spatial filters needed to extract significant features using the explained variance ratio (EVR) and employs a Subject-independent method for real-time processing of Emotion EEG data. The advantages of this method are as follows: (a) it automatically determines the spatial filter coefficients distinguishing emotional states and extracts the best features; (b) it is very robust for real-time analysis of new data using a subject-independent technique that considers subject sets, and not a specific subject; (c) it can be easily applied to both binary-class and multi-class data. Experimental results on real-world EEG emotion recognition tasks demonstrate that AsEmo outperforms other state-of-the-art methods with a 2-8% improvement in terms of classification accuracy.