Digital platforms could facilitate meditation practice by discriminating the participants’ mental state in real time based on neural activities. However, the search for neural correlates of meditative states yields contradictory results in the literature. To identify the neural signature of meditation, we propose a Random Forest classifier to discriminate between a Focused Attention Meditation (FAM) and a problem-solving task, based on two- second samples of EEG data. Two types of classifiers are considered: individual classifiers, trained on EEG data from the considered subject, and general classifiers, trained on inter- individual data. Our results show that the individual classifiers achieve superior performance with an average accuracy of 93% over 14 subjects. The general classifiers display a lower accuracy (74% and 54% depending on whether the data from the tested subject was included in the training set). This study suggests that automatic detection of meditative processes greatly benefits from intra-personal training. The most discriminating EEG features between the two tasks are the Beta mean band amplitude and the Theta-Gamma phase-amplitude coupling, particularly in the occipital and left centro-temporal brain regions. Our findings favor personalized classifiers for FAM.