Human personality reflects spatio-temporal and time-frequency EEG structure

Spatio-temporal dynamics of human EEG

Electroencephalogram ( E E G ) recording of spontaneous brain electrical activity resulting from collective dynamical behaviour of the neural mass was traditionally treated as a random signal and processed by stochastic methods like spectral analysis. Qualitatively new views were opened by approaches derived from synergetics, non-linear dynamics and theory of deterministic chaos introduced into...

Chapter 26 : Spatio - temporal models for EEG

Discovering Urban Spatio-temporal Structure from Time-Evolving Traffic Networks

The traffic networks reflect the pulse and structure of a city and shows some dynamic characteristic. Previous research in mining structure from networks mostly focus on static networks and fail to exploit the temporal patterns. In this paper, we aim to solve the problem of discovering the urban spatio-temporal structure from time-evolving traffic networks. We model the time-evolving traffic ne...

A Time-Frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...

Spatio-Temporal Correlation Tensors Reveal Functional Structure in Human Brain

Resting state functional magnetic resonance imaging (fMRI) has been commonly used to measure functional connectivity between cortical regions, while diffusion tensor imaging (DTI) can be used to characterize structural connectivity of white matter tracts. In principle combining resting state fMRI and DTI data could allow characterization of structure-function relations of distributed neural net...