Haptic Feedback Compared with Visual Feedback for BCI

Feedback plays an important role when learning to use a Brain-Computer Interface (BCI). Here we compare visual and haptic feedback in a short experiment. By imagining left and right hand movements, six subjects tried to control a BCI with the help of either visual or haptic feedback every 1s. Alpha band EEG signals from C3 and C4 were classified. The classifier was updated after each prediction using correct class information. Thus feedback could be given throughout the experiment. Subjects got better at controlling the BCI during the experiment independent of the feedback modality. Haptic feedback did not present any artifacts to the signals. More research is required on haptic feedback for BCI-applications because it frees visual attention to other tasks

Vibrotactile Feedback in the Context of Mu-Rhythm based BCI

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control. in which all parts reflected the computational and actuation process of an actual BCI, except the souce, which was simulated using a �noisy� PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task

Bayesian estimation of directed functional coupling from brain recordings

In many fields of science, there is the need of assessing the causal influences among time series. Especially in neuroscience, understanding the causal interactions between brain regions is of primary importance. A family of measures have been developed from the parametric implementation of the Granger criteria of causality based on the linear autoregressive modelling of the signals. We propose a new Bayesian method for linear model identification with a structured prior (GMEP) aiming to apply it as linear regression method in the context of the parametric Granger causal inference. GMEP assumes a Gaussian scale mixture distribution for the group sparsity prior and it enables flexible definition of the coefficient groups. Approximate posterior inference is achieved using Expectation Propagation for both the linear coefficients and the hyperparameters. GMEP is investigated both on simulated data and on empirical fMRI data in which we show how adding information on the sparsity structure of the coefficients positively improves the inference process. In the same simulation framework, GMEP is compared with others standard linear regression methods. Moreover, the causal inferences derived from GMEP estimates and from a standard Granger method are compared across simulated datasets of different dimensionality, density connection and level of noise. GMEP allows a better model identification and consequent causal inference when prior knowledge on the sparsity structure are integrated in the structured prior

EEG and MEG brain-computer interface for tetraplegic patients

Item does not contain fulltextWe characterized features of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated in the sensorimotor cortex of three tetraplegics attempting index finger movements. Single MEG and EEG trials were classified offline into two classes using two different classifiers, a batch trained classifier and a dynamic classifier. Classification accuracies obtained with dynamic classifier were better, at 75%, 89%, and 91% in different subjects, when features were in the 0.5-3.0-Hz frequency band. Classification accuracies of EEG and MEG did not differ

Physiological science of leptin: recent findings

Contains fulltext : 157290 .pdf (publisher's version ) (Closed access)Objective: We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. Methods: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. Results: In MDD patients, decreased theta (4–8 Hz) and alpha (8–14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14–30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r = 0.68, p = 0.0005) and beta power (r = 0.56, p = 0.007) respectively. Conclusions: Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. Significance: The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process.7 p

Mild cognitive impairment associates with concurrent decreases in serum cholesterol and cholesterol-related lipoprotein subclasses

Accumulating evidence suggests that serum lipids are associated with cognitive decline and dementias. However, majority of the existing information concerns only serum total cholesterol (TC) and data at the level of lipoprotein fractions and subclasses is limited. The aim of this study was to explore the levels and trends of main cholesterol and triglyceride measures and eight lipoprotein subclasses during normal aging and the development of mild cognitive impairment by following a group of elderly for six years

A multi-metabolite analysis of serum by H-1 NMR spectroscopy: Early systemic signs of Alzheimer's disease

Item does not contain fulltextA three-molecular-window approach for H-1 NMR spectroscopy of serum is presented to obtain specific molecular data on lipoproteins, Various low-molecular-weight metabolites, and individual lipid molecules together with their degree of (poly)(un)saturation. The multiple data were analysed with self-organising maps, illustrating the strength of the approach as a holistic metabonomics framework in solely data-driven metabolic phenotyping. We studied 180 serum samples of which 30% were related to mild cognitive impairment (MCI), a neuropsychological diagnosis with severely increased risk for Alzheimer's disease (AD). The results underline the association between MCI and the metabolic syndrome (MetS). Additionally, the low relative amount of omega-3 fatty acids appears more indicative of MCI than low serum omega-3 OF polyunsaturated fatty acid concentration as such. The analyses also feature the role of elevated glycoproteins in the risk for AD, Supporting the view that coexistence of inflammation and the MetS forms a high risk condition for cognitive decline.6 p