Teegi: Tangible EEG Interface

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, en- gaging and informative way. To this end, we have designed a new system based on a unique combination of spatial aug- mented reality, tangible interaction and real-time neurotech- nologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and infor- mative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.Comment: to appear in UIST-ACM User Interface Software and Technology Symposium, Oct 2014, Honolulu, United State

Diagnosis by Documentary: Professional Responsibilities in Informal Encounters

Most work addressing clinical workers' professional responsibilities concerns the norms of conduct within established professional-patient relationships, but such responsibilities may extend beyond the clinical context. We explore health workers' professional responsibilities in such "informal" encounters through the example of a doctor witnessing the misdiagnosis and mistreatment of a serious long-term condition in a television documentary, arguing that neither internalist approaches to professional responsibility (such as virtue ethics or care ethics) nor externalist ones (such as the "social contract" model) provide sufficiently clear guidance in such situations. We propose that a mix of both approaches, emphasizing the noncomplacency and practical wisdom of virtue ethics, but grounding the normative authority of virtue in an external source, is able to engage with the health worker's responsibilities in such situations to the individual, the health care system, and the population at large

Effects of force load, muscle fatigue and extremely low frequency magnetic stimulation on EEG signals during side arm lateral raise task

Objective: This study was to quantitatively investigate the effects of force load, muscle fatigue and extremely low frequency (ELF) magnetic stimulation on electroencephalography (EEG) signal features during side arm lateral raise task. Approach: EEG signals were recorded by a BIOSEMI Active Two system with Pin-Type active-electrodes from 18 healthy subjects when they performed the right arm side lateral raise task (90° away from the body) with three different loads (0 kg, 1 kg and 3 kg; their order was randomized among the subjects) on the forearm. The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as non-fatigue status and the last 10 s before the subject was exhausted as fatigue status. The subject was then given a 5 min resting between different loads. Two days later, the same experiment was performed on each subject except that ELF magnetic stimulation was applied to the subject's deltoid muscle during the 5 min resting period. EEG features from C3 and C4 electrodes including the power of alpha, beta and gamma and sample entropy were analyzed and compared between different loads, non-fatigue/fatigue status, and with/without ELF magnetic stimulation. Main results: The key results were associated with the change of the power of alpha band. From both C3-EEG and C4-EEG, with 1 kg and 3 kg force loads, the power of alpha band was significantly smaller than that from 0 kg for both non-fatigue and fatigue periods (all p    0.05 for all the force loads except C4-EEG with ELF simulation). The power of alpha band at fatigue status was significantly increased for both C3-EEG and C4-EEG when compared with the non-fatigue status (p    0.05, except between non-fatigue and fatigue with magnetic stimulation in gamma band of C3-EEG at 1 kg, and in the SampEn at 1 kg and 3 kg force loads from C4-EEG). Significance: Our study comprehensively quantified the effects of force, fatigue and the ELF magnetic stimulation on EEG features with difference forces, fatigue status and ELF magnetic stimulation

Sleep Analytics and Online Selective Anomaly Detection

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science. Scientists have segmented sleep into several stages and stage two is characterized by two patterns (or anomalies) in the EEG time series recorded on sleep subjects. These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was introduced to design a residual system, where all anomalies (known and unknown) are detected but the system only triggers an alarm when non-SS anomalies appear. The solution of the OSAD problem required us to combine techniques from both machine learning and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.Comment: Submitted to 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Mental states as macrostates emerging from brain electrical dynamics

Psychophysiological correlations form the basis for different medical and scientific disciplines, but the nature of this relation has not yet been fully understood. One conceptual option is to understand the mental as “emerging” from neural processes in the specific sense that psychology and physiology provide two different descriptions of the same system. Stating these descriptions in terms of coarser- and finer-grained system states (macro- and microstates), the two descriptions may be equally adequate if the coarse-graining preserves the possibility to obtain a dynamical rule for the system. To test the empirical viability of our approach, we describe an algorithm to obtain a specific form of such a coarse-graining from data, and illustrate its operation using a simulated dynamical system. We then apply the method to an electroencephalographic recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject

Causal hierarchy within the thalamo-cortical network in spike and wave discharges

Background: Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges. Methodology and Principal Findings: We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE) with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A), ventromedial prefrontal cortex (model B), and precuneus (model C). Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus), to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was ∼1. Conclusion: Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto) cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus - an index of awareness - and the occurrence of pathological discharges in epilepsy. © 2009 Vaudano et al

Experimental determination of the transient transport and of fluctuations relevant to transport in ASDEX

Particle transport was studied in ASDEZ with modulated puffing of the discharge gas and of impurities. The energy transport is investigated by numerical simulation of the heat pulse after the swatooth crash. Small scale density fluctuations are investigated in the confinement region with far infrared scattering and reflectometry and in the edge plasma with langmuir probes and Ha diagnostic. In addition to a diffuse component of the particle transport, a strong inward drift is observed in all discharges. In ohmic discharges the transport coefficients decrease and saturate like 1/TE with increasing density. They are smaller in deuterium that in hydrogen. In the improved ohmic confinement (IOC)regime mainly D in the outer region is reduced. D increases proportionally to the heating power in L-mode discharges. The improvement of particle confinement in the H-mode is explained by a increase of the inward drift at the edge rather than a decrease of D. The impurity diffusion coefficient is independent of the impurity mass and charge. In ohmic discharges, it varies with ne like the bulk diffusion coefficient, is independent of B or increases weakly with B and increases with Ip. In L-mode discharges, Dimp increases linearly with the heating power. The electron thermal condustivity determined by heat pulse propagation exceeds the stationary value by a factor of 3-4, assuming merely diffusive heat transport. Convection does not significantly reduce this factor. however, non-diagonal terms

The Nonlinear Dynamic Conversion of Analog Signals into Excitation Patterns

Local periodic perturbations induce frequency-dependent propagation waves in an excitable spatio-temporally chaotic system. We show how segments of noise-contaminated and chaotic perturbations induce characteristic sequences of excitations in the model system. Using a set of tuned excitable systems, it is possible to characterize signals by their spectral composition of excitation pattern. As an example we analyze an epileptic spike-and-wave time series.Comment: 14 pages, 5 figure