Different level of virtualization of sight and touch produces the uncanny valley of avatar's hand embodiment.

Humans increasingly often act through virtual and robotic avatars, which can feed back to their user only virtual sensory information. Since avatar is user's embodiment and body image is mostly based on senses, how virtualization of sensory inputs affects avatar self-attribution is a key question for understanding nowadays human behavior. By manipulating visual and tactile inputs in a series of experiments fashioned after the rubber hand illusion, we assessed the relative weight of the virtualization of sight (Real, Robotic, Virtual) and of touch (Real, Virtual) on artificial hand embodiment. Virtualization decreased embodiment, but unexpectedly lowest embodiment was found when only one sense was virtual. Discordant levels of virtualization of sight and touch elicited revulsion, extending the concept of the uncanny valley to avatar embodiment. Besides timing, spatial constraints and realism of feedback, a matched degree of virtualization of seen and felt stimuli is a further constraint in building the representation of the body.

Does an intraneural interface short-term implant for robotic hand control modulate sensorimotor cortical integration? An EEG-TMS co-registration study on a human amputee.

PURPOSE: Following limb amputation, central and peripheral nervous system relays partially maintain their functions and can be exploited for interfacing prostheses. The aim of this study is to investigate, for the first time by means of an EEG-TMS co-registration study, whether and how direct bidirectional connection between brain and hand prosthesis impacts on sensorimotor cortical topography. METHODS: Within an experimental protocol for robotic hand control, a 26 years-old, left-hand amputated male was selected to have implanted four intrafascicular electrodes (tf-LIFEs-4) in the median and ulnar nerves of the stump for 4 weeks. Before tf-LIFE-4s implant (T0) and after the training period, once electrodes have been removed (T1), experimental subject's cortico-cortical excitability, connectivity and plasticity were tested via a neuronavigated EEG-TMS experiment. RESULTS: The statistical analysis clearly demonstrated a significant modulation (with t-test p < 0.0001) of EEG activity between 30 and 100 ms post-stimulus for the stimulation of the right hemisphere. When studying individual latencies in that time range, a global amplitude modulation was found in most of the TMS-evoked potentials; particularly, the GEE analysis showed significant differences between T0 and T1 condition at 30 ms (p < 0.0404), 46 ms (p < 0.0001) and 60 ms (p < 0.007) latencies. Finally, also a clear local decrement in N46 amplitude over C4 was evident. No differences between conditions were observed for the stimulation of the left hemisphere. CONCLUSIONS: The results of this study confirm the hypothesis that bidirectional neural interface could redirect cortical areas -deprived of their original input/output functions- toward restorative neuroplasticity. This reorganization strongly involves bi-hemispheric networks and intracortical and transcortical modulation of GABAergic inhibition.

Baseline wander estimation and removal by quadratic variation reduction.

The baseline wander is a low frequency additive noise partially overlapping the band of ECG signal. This makes its removal difficult without affecting the ECG. In this work we propose a novel approach to baseline wander estimation and removal based on the notion of quadratic variation. The quadratic variation is a suitable index of variability for vectors and sampled functions. We derive an algorithm for baseline estimation solving a constrained convex optimization problem. The computational complexity of the algorithm is linear in the size of the ECG record to detrend, making it suitable for realtime applications. Simulation results confirm the effectiveness of the approach and highlight its ability to remove baseline wander. Eventually, the proposed algorithm is not limited to ECG signals, but can be effectively applied whenever baseline estimation and removal are needed, such as EEG records.

Denoising and harmonic artifacts rejection for ECG P-waves by quadratic variation reduction.

Atrial fibrillation (AF) is a common cardiac arrhythmia related to irregular atrial contractions. Several studies have shown that the analysis of P-waves extracted from ECG signals is helpful in understanding the predisposing factors to AF. However, P-waves are usually highly corrupted by noise and harmonic artifacts and this makes quite difficult their analysis. Recently we proposed a novel algorithm for denoising P-waves based on the notion of quadratic variation reduction. It is quite good in denoising P-waves affected by noise, but its effectiveness reduces when it is used in filtering out harmonic artifacts, like power-line interference. In this paper we propose an algorithm that overcomes this limitation and extends our previous method allowing it to both denoise and reject harmonic artifacts. Simulation results confirm the effectiveness of the approach and highlight its ability to remove both noise and artifacts. The algorithm has reduced computational complexity and this makes it suitable for real-time applications.