Abstract: Systems and methods of the present invention may relate to prediction of a mental health score, representing a mental condition of a speaker. Embodiments of the invention may analyze speech by receiving an audio data element representing a discussion; extracting a first set of audio segments pertaining to speech of a first speaker in the discussion; analyzing the first set of audio segments to produce a set of audio features; and applying a machine-learning (ML) model on the set of audio features, to predict a mental health score, representing a mental condition of the first speaker. Embodiments of the invention may provide technical means for supporting psychiatric assessment of mental disorders such as depression and anxiety in an automatic, nondisruptive manner, e.g., without direct patient's collaboration, thereby increasing validity of the assessment.

Abstract: There is provided a method for simulating at least one wet electroencephalogram (EEG) signal from at least one dry EEG signals, comprising: receiving at least one dry EEG signal measured by dry EEG electrode(s) applied to locations on a head of a patient corresponding to at least one of temporal lobe or frontal lobe, computing at least one simulated wet EEG signal by applying a trained statistical model based on at least one dry EEG signal, the at least one simulated wet EEG signal simulating wet EEG signals measured by a wet EEG electrode applied to at least one location corresponding to at least one of: central, parietal lobe, and occipital lobe, and providing the at least one simulated wet EEG signal for adjusting feedback of a neurofeedback treatment applied by a neurofeedback device.

Abstract: There is provided a method of selecting subpopulations of users mapped to subpopulations of entities, comprising: receiving latent factors of a mapping between users and entities and a predicted correlation value for each undefined mapping, computed by a recommender process, for each respective latent factor: identifying, by a user semantic model, user features of the users correlated to the respective latent factor, identifying, by an entity semantic model, entity features of the entities correlated to the respective latent factor, generate combinations of pairs each including one user feature and one entity feature, for each pair, compute statistical metric(s) indicative of a change relative to the predicted correlation value for the users and the entities, select pair(s) according to a requirement of the statistical metric(s), and provide the user feature and the entity feature for each selected pair.

Abstract: There is provided a method for simulating at least one wet electroencephalogram (EEG) signal from at least one dry EEG signals, comprising: receiving at least one dry EEG signal measured by dry EEG electrode(s) applied to locations on a head of a patient corresponding to at least one of temporal lobe or frontal lobe, computing at least one simulated wet EEG signal by applying a trained statistical model based on at least one dry EEG signal, the at least one simulated wet EEG signal simulating wet EEG signals measured by a wet EEG electrode applied to at least one location corresponding to at least one of: central, parietal lobe, and occipital lobe, and providing the at least one simulated wet EEG signal for adjusting feedback of a neurofeedback treatment applied by a neurofeedback device.