A Novel Wearable and Wireless Device to Investigate Perception in Interactive Scenarios.

The aim of the present work is to introduce a novel wearable device suitable to be used to investigate perception in interactive tasks, on individuals with and without sensory disabilities. The system is composed by small units embedded with sensors and actuators that allows emitting different kind of stimuli (light, haptic, sound) and to record the user response, thanks to a capacitive sensor. We validated the system by implementing an interception task in three different sensory modalities: visual, tactile and auditory. Six subjects with normal sight were asked to tap either a static or a moving stimulus generated by 6 units placed on their forearm. Results suggest that the system can effectively provide new insights in characterizing how perception principles vary when perceptual judgement occurs through different senses. This confirms the device potential in contributing to the design of rehabilitation protocols rooted on neuroscientific findings, for people with sensory impairments.

The Effect of Vibrotactile Feedback on ErrP-based Adaptive Classification of Motor Imagery.

This work presents an implementation of Error-related Potential (ErrP) detection to produce progressive adaptation of a motor imagery task classifier. The main contribution is in the evaluation of the effect of vibrotactile feedback on both ErrP and motor imagery detection. Results confirm the potential of self-adaptive techniques to improve motor imagery classification, and support the design of vibratory and in general tactile feedback into Brain-Computer Interfaces to improve both static and adaptive performance.

Mutual information-based feature selection for low-cost BCIs based on motor imagery.

In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system. The minimal sub set of features most relevant to task description and less redundant to each other was determined, and the corresponding classification accuracy was assessed offline employing linear support vector machine (SVM) in a 10-fold cross validation scheme. The analysis was performed: (a) on the original full Dataset I from BCI competition IV, (b) on a restricted channels set from Dataset I corresponding to available Emotiv EPOC electrodes locations, and (c) on data recorded with the EPOC system. Results from (a) showed that an offline classification accuracy above 80% can be reached using only 5 features. Limiting the analysis to EPOC channels caused a decrease of classification accuracy, although it still remained above chance level, both for data from (b) and (c). A top accuracy of 70% was achieved using 2 optimal features. These results encourage further research towards the development of portable low cost motor imagery-based BCI systems.

Extended Granger causality: a new tool to identify the structure of physiological networks.

Granger causality (GC) is a very popular tool for assessing the presence of directional interactions between two time series of a multivariate data set. In its original formulation, GC does not account for zero-lag correlations possibly existing between the observed time series. In the present study we compare the GC with a novel measure, termed extended GC (eGC), able to capture instantaneous causal relationships. We present a two-step procedure for the practical estimation of eGC based on first detecting the existence of zero-lag correlations, and then assigning them to one of the two possible causal directions using pairwise measures of non-Gaussianity. The proposed method was validated in a simulation study, showing that the estimation procedure based on the extended representation overcomes the limits of the classic computation of GC, correctly detecting the presence and direction of zero-lag interactions and providing a meaningful causal interpretation based on the eGC. Then, GC and eGC were computed on the physiological variability series of heart period (HP), mean arterial pressure (AP) and cerebral blood flow velocity (FV) in ten subjects with postural related syncope (PRS), during different epochs of an head-up tilt test protocol. We found that both measures reflect the baroreflex impairment and the loss of cerebral autoregulation during pre-syncope. Furthermore, eGC analysis suggests that fast, within-beat effects between AP and FV variability contribute substantially to the mutual regulation of these physiological variables, and may play an important role in the impairment of cerebrovascular regulation associated with PRS.