This study investigates dynamic time warping (DTW) as a possible analysis method for EEG-based affective computing in a self-paced learning task in which inter- and intrapersonal differences are large. In one experiment, participants (N=200) carried out an implicit category learning task where their frontal EEG signals were collected throughout the experiment. Using DTW, we measured the dissimilarity distances of EEG signals between participants and examined the extent to which a k-Nearest Neighbors algorithm could predict self-rated feelings of a participant from signals taken from other participants (between-participants prediction). Results showed that DTW provides potentially useful characteristics for EEG data analysis in a heterogeneous setting. In particular, theorybased segmentation of time-series data were particularly useful for DTW analysis while smoothing and standardization were detrimental when applied in a self-paced learning task.