Empirical Characterization of the Temporal Dynamics of EEG Spectral Components

The properties of time-domain electroencephalographic data have been studied extensively. There has however been no attempt to characterize the temporal evolution of resulting spectral components when successive segments of electroencephalographic data are decomposed. We analyzed resting-state scalp electroencephalographic data from 23 subjects, acquired at 256 Hz, and transformed using 64-point Fast Fourier Transform with a Hamming window. KPSS and Nason tests were administered to study the trend- and wide sense stationarity respectively of the spectral components. Thereafter, the Rosenstein algorithm for dynamic evolution was applied to determine the largest Lyapunov exponents of each component’s temporal evolution. We found that the evolutions were wide sense stationary for time scales up to 8 s, and had significant interactions, especially between spectral series in the frequency ranges 0–4 Hz, 12–24 Hz, and 32-128 Hz. The spectral series were generally non-chaotic, with average largest Lyapunov exponent of 0. The results show that significant information is contained in all frequency bands, and that the interactions between bands are complicated and time-varying

The blessing of Dimensionality : feature selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation

Functional connectivity (FC) characterizes brain activity from a multivariate set of N brain signals by means of an NxN matrix A, whose elements estimate the dependence within each possible pair of signals. Such matrix can be used as a feature vector for (un)supervised subject classification. Yet if N is large, A is highly dimensional. Little is known on the effect that different strategies to reduce its dimensionality may have on its classification ability. Here, we apply different machine learning algorithms to classify 33 children (age [6-14 years]) into two groups (healthy controls and Attention Deficit Hyperactivity Disorder patients) using EEG FC patterns obtained from two phase synchronisation indices. We found that the classification is highly successful (around 95%) if the whole matrix A is taken into account, and the relevant features are selected using machine learning methods. However, if FC algorithms are applied instead to transform A into a lower dimensionality matrix, the classification rate drops to less than 80%. We conclude that, for the purpose of pattern classification, the relevant features should be selected among the elements of A by using appropriate machine learning algorithms

Proceedings of Universiti Sains Malaysia International Conference on Social Sciences 2015 - Touching Lives, Bridging Society

A society and people in general have always been the major focus of any study in the field of social sciences. Given the conflicts, competition, pressures, tensions and differences that are prevalent and persisting around us in today‘s society, social scientists can have a great potential to touch the lives of all human beings in many different societies. By doing so, they too can bridge the gaps that are seen to be widening between and within societies today. The field of social sciences critically examines and analyses elements of human existence, their infinite combinations of interactions, their value systems, traditions, culture, options and choices and practices. This is reflected in a broad range of disciplines which include anthropology and sociology, development studies, economics, political science, psychology and social work. The proceedings of this conference includes papers presented at the USM-International Conference on Social Sciences (USM-ICOSS) 2015 which was hosted with the aptly noble theme ―Touching Lives and Bridging Society‖ and held from 27 - 28 August 2015 at the Rainbow Paradise Beach Resort, Penang, Malaysia. This conference is solely organized by the School of Social Sciences, Universiti Sains Malaysia (USM), Malaysia

University catalog, 2018-19

Welcome to the University of Missouri 2018-2019 catalog! We are pleased to provide an interactive and searchable catalog online. The catalog is a comprehensive reference for your academic studies. It includes a list of all degree programs offered at MU, including bachelors, masters, specialists, doctorates, minors, certificates, and emphasis areas. It details the university wide requirements, the curricular requirements for each program, and in some cases provides a sample plan of study. The catalog includes a complete listing and description of approved courses. It also provides information on academic policies, contact information for supporting offices, and a complete listing of faculty members. Information in the catalog is current as of May 2018.--Page 17