Extra-powerful on the visuo-perceptual space, but variable on the number space: Different effects of optokinetic stimulation in neglect patients

We studied the effects of optokinetic stimulation (OKS; leftward, rightward, control) on the visuo-perceptual and number space, in the same sample, during line bisection and mental number interval bisection tasks. To this end, we tested six patients with right-hemisphere damage and neglect, six patients with right-hemisphere damage but without neglect, and six neurologically healthy participants. In patients with neglect, we found a strong effect of leftward OKS on line bisection, but not on mental number interval bisection. We suggest that OKS influences the number space only under specific conditions

DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters - a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter's radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets - collected in static and, for the first time, dynamic conditions - and the code database with the community.Comment: To be presented at the 42nd IEEE International Conference on Distributed Computing Systems (ICDCS), Bologna, Italy, July 10-13, 202

The relationship between Cognitive Reserve and Math Abilities

Cognitive Reserve is the capital of knowledge and experiences that an individual acquires over their life-span. Cognitive Reserve is strictly related to Brain Reserve, which is the ability of the brain to cope with damage. These two concepts could explain many phenomena such as the modality of onset in dementia or the different degree of impairment in cognitive abilities in aging. The aim of this study is to verify the effect of Cognitive Reserve, as measured by a questionnaire, on a variety of numerical abilities (number comprehension, reading and writing numbers, rules and principles, mental calculations and written calculations), in a group of healthy older people (aged 65-98 years). Sixty older individuals were interviewed with the Cognitive Reserve Index questionnaire (CRIq), and assessed with the Numerical Activities of Daily Living battery (NADL), which included formal tasks on math abilities, an informal test on math, one interview with the participant, and one interview with a relative on the perceived math abilities. We also took into account the years of education, as another proxy for Cognitive Reserve. In the multiple regression analyses on all formal tests, CRIq scores did not significantly predict math performance. Other variables, i.e., years of education and Mini-Mental State Examination score, accounted better for math performance on NADL. Only a subsection of CRIq, CRIq-Working-activity, was found to predict performance on a NADL subtest assessing informal use of math in daily life. These results show that education might better explain abstract math functions in late life than other aspects related to Cognitive Reserve, such as lifestyle or occupational attainment

Multi-tasking uncovers right spatial neglect and extinction in chronic left-hemisphere stroke patients

open7noUnilateral Spatial Neglect, the most dramatic manifestation of contralesional space unawareness, is a highly heterogeneous syndrome. The presence of neglect is related to core spatially lateralized deficits, but its severity is also modulated by several domain-general factors (such as alertness or sustained attention) and by task demands. We previously showed that a computer-based dual-task paradigm exploiting both lateralized and non-lateralized factors (i.e., attentional load/multitasking) better captures this complex scenario and exacerbates deficits for the contralesional space after right hemisphere damage. Here we asked whether multitasking would reveal contralesional spatial disorders in chronic left hemisphere damaged (LHD) stroke patients, a population in which impaired spatial processing is thought to be uncommon. Ten consecutive LHD patients with no signs of right-sided neglect at standard neuropsychological testing performed a computerized spatial monitoring task with and without concurrent secondary tasks (i.e., multitasking). Severe contralesional (right) space unawareness emerged in most patients under attentional load in both the visual and auditory modalities. Multitasking affected the detection of contralesional stimuli both when presented concurrently with an ipsilesional one (i.e., extinction for bilateral targets) and when presented in isolation (i.e., left neglect for right-sided targets). No spatial bias emerged in a control group of healthy elderly participants, who performed at ceiling, as well as in a second control group composed of patients with Mild Cognitive Impairment. We conclude that the pathological spatial asymmetry in LHD patients cannot be attributed to a global reduction of cognitive resources but it is the consequence of unilateral brain damage. Clinical and theoretical implications of the load-dependent lack of awareness for contralesional hemispace following LHD are discussed.embargoed_20180601Blini, Elvio; Romeo, Zaira; Spironelli, Chiara; Pitteri, Marco; Meneghello, Francesca; Bonato, Mario; Zorzi, MarcoBlini, ELVIO ADALBERTO; Romeo, Zaira; Spironelli, Chiara; Pitteri, Marco; Meneghello, Francesca; Bonato, Mario; Zorzi, Marc

Towards Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

As Wi-Fi becomes ubiquitous in public and private spaces, it becomes natural to leverage its intrinsic ability to sense the surrounding environment to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking. For this reason, the IEEE 802.11bf Task Group is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant devices. However, the new standard is expected to leave the specific sensing algorithms open to implementation. To fill this gap, this article explores the practical implications of integrating sensing and communications into Wi-Fi networks. We provide an overview of the support that will enable sensing applications, together with an in-depth analysis of the role of different devices in a Wi-Fi sensing system and a description of the open research challenges. Moreover, an experimental evaluation with off-the-shelf devices provides suggestions about the parameters to be considered when designing Wi-Fi sensing systems. To make such an evaluation replicable, we pledge to release all of our dataset and code to the community

Il ruolo del tratto di numero nella comprensione delle frasi relative oggetto in pazienti afasici italiani

This study investigates the role of number features in the agrammatic comprehension of object relative clauses with preverbal subjects. A sentence-picture matching task was used to assess the performance of four Italian aphasic patients. Results show that the mismatch condition of number features (with the subject singular/plural and the object plural/singular) is computationally the most complex one. In this condition, aphasic patients have greatest difficulties in establishing the right set of syntactic relations among the constituents of the sentence

Optokinetic Stimulation Modulates Neglect for the Number Space: Evidence from Mental Number Interval Bisection

Behavioral, neuropsychological, and neuroimaging data support the idea that numbers are represented along a mental number line (MNL), an analogical, visuospatial representation of number magnitude. The MNL is left-to-right oriented in Western cultures, with small numbers on the left and larger numbers on the right. Left neglect patients are impaired in the mental bisection of numerical intervals, with a bias toward larger numbers that are relatively to the right on the MNL. In the present study we investigated the effects of optokinetic stimulation (OKS) – a technique inducing visuospatial attention shifts by means of activation of the optokinetic nystagmus – on number interval bisection. One patient with left neglect following right-hemisphere stroke (BG) and four control patients with right-hemisphere damage, but without neglect, performed the number interval bisection task in three conditions of OKS: static, leftward, and rightward. In the static condition, BG misbisected to the right of the true midpoint. BG misbisected to the left following leftward OKS, and again to the right of the midpoint following rightward OKS. Moreover, the variability of BG’s performance was smaller following both leftward and rightward OKS, suggesting that the attentional bias induced by OKS reduced the “indifference zone” that is thought to underlie the length effect reported in bisection tasks. We argue that shifts of visuospatial attention, induced by OKS, may affect number interval bisection, thereby revealing an interaction between the processing of the perceptual space and the processing of the number space

Efficacy of a Training on Executive Functions in Potentiating Rehabilitation Effects in Stroke Patients

Cognitive impairment after a stroke has a direct impact on patients\u2019 disability. In particular, impairment of Executive Functions (EFs) interferes with re\u2010adaptation to daily life. The aim of this study was to explore whether adding a computer\u2010based training on EFs to an ordinary rehabilitation program, regardless of the specific brain damage and clinical impairment (motor, language, or cognitive), could improve rehabilitation outcomes in patients with stroke. An EF training was designed to have minimal motor and expressive language demands and to be applied to a wide range of clinical conditions. A total of 37 stroke patients were randomly assigned to two groups: a training group, which performed the EF training in addition to the ordinary rehabilitation program (treatment as usual), and a control group, which performed the ordinary rehabilitation exclusively. Both groups were assessed before and after the rehabilitation program on neuropsychological tests covering multiple cognitive domains, and on functional scales (Barthel index, Functional Independence Measure). The results showed that only patients who received the training improved their scores on the Attentional Matrices and Phonemic Fluency tests after the rehabilitation program. Moreover, they showed a greater functional improvement in the Barthel scale as well. These results suggest that combining an EF training with an ordinary rehabilitation program potentiates beneficial effects of the latter, especially in promoting independence in activities of daily living

A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11 ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms

Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical towards achieving robustness and accuracy in multi-user settings. Our technique is tested both on a single-target public dataset, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.Comment: 16 pages, 12 figures, 5 table