This paper aims at developing adaptive methods for electroencephalogram (EEG) signal segmentation in the time-frequency domain, in order to effectively retrieve the emotion-related information within the EEG recordings. Using the multidimensional directed information analysis supported by the frontal brain asymmetry in the case of emotional reaction, a criterion, namely asymmetry index , is used to realize the proposed segmentation processes that take into account both the time and frequency (in the empirical mode decomposition domain) emotionally related EEG components. The efficiency of the -based “emotional” filters was justified through an extensive classification process, using higher-order crossings and cross-correlation as feature-vector extraction techniques and a support vector machine classifier for six different classification scenarios in the valence/arousal space. This resulted in mean classification rates from 64.17% up to 82.91% in a user-independent base, revealing the potential of establishing such a filtering for reliable EEG-based emotion recognition systems.