Anwesha Sengupta

Complex brain networks using Visibility Graph synchronization

Coordination between brain areas has been extensively studied by analyzing the synchronization of EEG signals generated in these areas. This paper proposes a complex network structure of the brain using the Visibility Graph Similarity technique to study the change of connectivity among brain areas at various stages of an experiment involving sleep-deprived subjects. The parameters of the network have also been compared with that of a complex network with a randomly chosen degree of connectivity.

A Multimodal System for Assessing Alertness Levels Due to Cognitive Loading

This paper proposes a scheme for assessing the alertness levels of an individual using simultaneous acquisition of multimodal physiological signals and fusing the information into a single metric for quantification of alertness. The system takes electroencephalogram, high-speed image sequence, and speech data as inputs. Certain parameters are computed from each of these measures as indicators of alertness and a metric is proposed using a fusion of the parameters for indicating alertness level of an individual at an instant. The scheme has been validated experimentally using standard neuropsychological tests, such as the Visual Response Test (VRT), Auditory Response Test (ART), a Letter Counting (LC) task, and the Stroop Test. The tests are used both as cognitive tasks to induce mental fatigue as well as tools to gauge the present degree of alertness of the subject. Correlation between the measures has been studied and the experimental variables have been statistically analyzed using measures such as multivariate linear regression and analysis of variance. Correspondence of trends obtained from biomarkers and neuropsychological measures validate the usability of the proposed metric.

Estimation of Fatigue in Drivers by Analysis of Brain Networks

Synchronization measures between Electro-Encephalogram (EEG) signals from different regions of the brain characterize the integration and segregation of brain areas during any mental and physical activity. EEG can reflect both the normal and abnormal activity of the brain and is widely used as a powerful tool in the field of clinical neurophysiology. Being sensitive to decreasing alertness and decline in vigilance, EEG can be used to predict performance degradation due to mental or physical fatigue. This paper studies the variation of fatigue levels in human drivers in a sleep-deprivation experiment by analyzing the synchronization between EEG recorded from brain areas. A Weighted Visibility Graph (WVG) technique has been proposed to quantify the synchronization between brain regions, which is then formulated in terms of a complex network. The change in the parameters of the network is analyzed to find the variation of connectivity and hence to trace the increase in fatigue levels of an individual.

Brain networks using nonlinear interdependence-based EEG synchronization: A study of human fatigue

Degradation in performance of human subjects due to mental or physical fatigue can be suitably predicted by the use of the electroencephalogram (EEG). Synchronization measures between EEG signals from different regions of the brain are often employed to characterize the interaction of brain areas during mental and physical activity. Analysis of fatigue induced by loss of sleep using EEG synchronization presents a promising field of research. The present paper employs Nonlinear Interdependencebased synchronization between EEG data recorded from various brain areas to analyze advancing levels of fatigue in human drivers in a sleep-deprivation experiment. The synchronization values are used to form a brain network at each stage of the experiment and values of parameters from networks corresponding to different brain regions have been compared to study the variation in connectivity between brain regions along successive stages of the experiment.

EEG Synchronization and Brain Networks: A Case Study in Fatigue

Synchronization measures between Electroencephalograph (EEG) signals from different regions of the brain are often employed to characterize the interaction of brain areas during mental and physical activity. This work examines the variation of alertness of human subjects due to fatigue in a sleep-deprivation experiment involving a simulated driving task using a Horizontal Visibility Graph (HVG)-based synchronization measure for EEG signals. The synchronization values are used to form a brain network at each stage of the experiment and network parameter values from various brain regions are compared to study the variation in connectivity between brain regions along successive stages of the experiment.

Analysis of fatigue using EEG state-space analysis

Electroencephalogram (EEG) is widely used to predict performance degradation of human subjects due to mental or physical fatigue. Analysis of fatigue due to sleep deprivation using EEG synchronization is a promising field of research. The present paper analyses advancing levels of fatigue in human drivers in a sleep-deprivation experiment by studying the synchronization between EEG data recorded from various brain areas. The S-Estimator technique based on state-space analysis has been employed to quantify the synchronization between EEG channels, which has then been formulated in terms of a complex network. The change in the parameters of the network has been analyzed to find the variation of connectivity between brain areas and hence to trace the increase in fatigue levels of the subjects.

Dielectric permittivity and electrical conductivity in magnetic nanoassemblies in c-ZrO 2 upon adding Cr 4+ (3d 2 ) spins

Inclusion of magnetic spins such as Cr⁴⁺(3d²) in cubic zirconia c-ZrO<inf>2</inf>, a dielectric oxygen ion conductor, is useful to tailor functional dielectric, electrical, and magnetic properties in a single system for several applications. A simple liquid polymer precursor containing Cr⁴⁺ and Zr⁴⁺ ions in a dispersed structure via glycerol is explored to prepare a solid solution CrO<inf>2</inf>-ZrO<inf>2</inf> at a moderate pressure 1.5 atm. A recovered powder when heated at moderate temperature 500–800 °C in ambient air yields a single phase compound of small crystallites (5–10 nm) in a c-ZrO<inf>2</inf> like stabilized phase. In this process, as much CrO<inf>2</inf> as 20 mol% has been dissolved. At room temperature, a sample having 20 mol% CrO<inf>2</inf> (heated at 500 °C) exhibits a magnetic hysteresis loop with large coercivity 794 Oe, but small saturation magnetization 1.64 emu/g and remanent ratio 0.253. At low frequencies such as ∼1 kHz, the dielectric permittivity (ε<inf>r</inf>) stays nearly constant ∼30 (close to the value 35 in pure c-ZrO<inf>2</inf>) over a wide temperature range 30–200 °C before it increases regularly with temperature, showing a value 183 at 500 °C. Also stable ac conductivity in this temperature range rises progressively by heating above 200 °C, with as large as five-orders of enhanced value 10⁻³ Sm⁻¹ by heating to 500 °C. In a model two phase system, the ε<inf>r</inf>-value arises from the conductivity. Both the properties reveal an inflection point in the Curie transition point near 275 °C.