Emotions are important in human-computer interaction. Emotions could be classified based on 3-dimensional Valence-Arousal-Dominance model which allows defining any number of emotions even without discrete emotion labels. In this paper, we proposed a real-time EEG-based subject-dependent valence level recognition algorithm, where the thresholds were used to identify different levels of the valence dimension of the human emotion. The algorithm was tested by using the EEG data labeled with valence levels. The algorithm could identify valence levels continuously. The algorithm was tested with the experiment data and with the benchmark affective EEG database DEAP where up to 9 levels of valence dimension with high/low dominance were recognized. Then, the algorithm was applied to recognize 16 emotions defined by high/low arousal, high/low dominance and 4 levels of valence. At least 14 electrodes should be used to get the better accuracy. The proposed algorithm could be implemented in different real-time applications such as emotional avatar and E-learning systems.