Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Adolescent Loneliness and Social Skills:Agreement and Discrepancies Between Self-, Meta-, and Peer-Evaluations

Contains fulltext : 160961.pdf (publisher's version ) (Open Access)Lonely adolescents report that they have poor social skills, but it is unknown whether this is due to an accurate perception of a social skills deficit, or a biased negative perception. This is an important distinction, as actual social skills deficits require different treatments than biased negative perceptions. In this study, we compared self-reported social skills evaluations with peer-reported social skills and meta-evaluations of social skills (i.e., adolescents' perceptions of how they believe their classmates evaluate them). Based on the social skills view, we expected negative relations between loneliness and these three forms of social skills evaluations. Based on the bias view, we expected lonely adolescents to have more negative self- and meta-evaluations compared to peer-evaluations of social skills. Participants were 1342 adolescents (48.64 % male, M age = 13.95, SD = .54). All classmates rated each other in a round-robin design to obtain peer-evaluations. Self- and meta-evaluations were obtained using self-reports. Data were analyzed using polynomial regression analyses and response surface modeling. The results indicated that, when self-, peer- and meta-evaluations were similar, a greater sense of loneliness was related to poorer social skills. Loneliness was also related to larger discrepancies between self- and peer-evaluations of loneliness, but not related to the direction of these discrepancies. Thus, for some lonely adolescents, loneliness may be related to an actual social skills deficit, whereas for others a biased negative perception of one's own social skills or a mismatch with the environment may be related to their loneliness. This implies that different mechanisms may underlie loneliness, which has implications for interventions.11 p