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With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recognition tasks in electroencephalography (EEG). EEG is a multi-channel time-series that provides information about the individual brain activity for diagnostics, neuro-rehabilitation, and other applications (including emotions recognition). Two main issues challenge the existing DL-based modeling methods for EEG: the high variability between subjects and the low signal-to-noise ratio making it difficult to ensure a good quality in the EEG data. In this paper, we propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction. We properly designed their architectures using the blocks of the well-known EEGNet as the encoder, and proposed a loss function based on dynamic time warping. We tested the models on the public Dataset 2a - BCI Competition IV, where EEG was collected from 9 subjects and 22 channels. hvEEGNet was found to reconstruct the EEG data with very high-fidelity, outperforming most previous solutions (including our vEEGNet-ver3 ). Furthermore, this was consistent across all subjects. Interestingly, hvEEGNet made it possible to discover that this popular dataset includes a number of corrupted EEG recordings that might have influenced previous literature results. We also investigated the training behaviour of our models and related it with the quality and the size of the input EEG dataset, aiming at opening a new research debate on this relationship. In the future, hvEEGNet could be used as anomaly (e.g., artefact) detector in large EEG datasets to support the domain experts, but also the latent representations it provides could be used in other classification problems and EEG data generation.

Electroencephalografic (EEG) data are complex multi-dimensional time-series that are very useful in many applications, from diagnostics to driving brain-computer interface systems. Their classification is still a challenging task, due to the inherent within- and between-subject variability and their low signal-to-noise ratio. On the other hand, the reconstruction of raw EEG data is even more difficult because of the high temporal resolution of these signals. Recent literature has proposed numerous machine and deep learning models that could classify, e.g., different types of movements, with an accuracy in the range 70% to 80% (with 4 classes). On the other hand, a limited number of works targeted the reconstruction problem, with very limited results. In this work, we propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised module based on variational autoencoders to extract a latent representation of the data, and a supervised module based on a feed-forward neural network to classify different movements. To build the encoder and the decoder of VAE we exploited the well-known EEGNet network. We implemented two slightly different architectures of vEEGNet, thus showing state-of-the-art classification performance, and the ability to reconstruct both low-frequency and middle-range components of the raw EEG. Although preliminary, this work is promising as we found out that the low-frequency reconstructed signals are consistent with the so-called motor-related cortical potentials, well-known motor-related EEG patterns and we could improve over previous literature by reconstructing faster EEG components, too. Further investigations are needed to explore the potentialities of vEEGNet in reconstructing the full EEG data, generating new samples, and studying the relationship between classification and reconstruction performance.

Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control for people suffering from neuro-motor impairments, with a special effort to bring these systems out of the controlled lab environments. Hence, accurately classifying MI from brain signals, e.g., from electroencephalography (EEG), is essential to obtain reliable BCI systems. However, MI classification is still a challenging task, because the signals are characterized by poor SNR, high intra-subject and cross-subject variability. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques, e.g., filter bank common spatial pattern (FBCSP), to extract subject-independent features and to increase the cross-subject classification performance of MI BCI systems. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We show-case the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with FBCSP in a 4-class MI classification over public datasets. To explore its cross-subject classification ability, we applied three different cross-validation schemes. From our results, we demonstrate that DynamicNet-implemented EEGNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied.

Wireless body area networks (WBANs) are becoming increasingly popular as they allow individuals to continuously monitor their vitals and physiological parameters remotely from the hospital. With the spread of the SARS-CoV-2 pandemic, the availability of portable pulse-oximeters and wearable heart rate detectors has boomed in the market. At the same time, in 2020 we assisted to an unprecedented increase of healthcare breaches, revealing the extreme vulnerability of the current generation of WBANs. Therefore, the development of new security protocols to ensure data protection, authentication, integrity and privacy within WBANs are highly needed. Here, we targeted a WBAN collecting ECG signals from different sensor nodes on the individual's body, we extracted the inter-pulse interval (i.e., R-R interval) sequence from each of them, and we developed a new information theoretic key agreement protocol that exploits the inherent randomness of ECG to ensure authentication between sensor pairs within the WBAN. After proper pre-processing, we provide an analytical solution that ensures robust authentication; we provide a unique information reconciliation matrix, which gives good performance for all ECG sensor pairs; and we can show that a relationship between information reconciliation and privacy amplification matrices can be found. Finally, we show the trade-off between the level of security, in terms of key generation rate, and the complexity of the error correction scheme implemented in the system.

As the worldwide population gets increasingly aged, in-home telemedicine and mobile-health solutions represent promising services to promote active and independent aging and to contribute to a paradigm shift towards patient-centric healthcare. In this work, we present ACTA (Advanced Cognitive Training for Aging), a prototype mobile-health solution to provide advanced cognitive training for senior citizens with mild cognitive impairments. We disclose here the conceptualization of ACTA as the integration of two promising rehabilitation strategies: the "Nudge theory", from the cognitive domain, and the neurofeedback, from the neuroscience domain. Moreover, in ACTA we exploit the most advanced machine learning techniques to deliver customized and fully adaptive support to the elderly, while training in an ecological environment. ACTA represents the next-step beyond SENIOR, an earlier mobile-health project for cognitive training based on Nudge theory, currently ongoing in Lombardy Region. Beyond SENIOR, ACTA represents a highly-usable, accessible, low-cost, new-generation mobile-health solution to promote independent aging and effective motor-cognitive training support, while empowering the elderly in their own aging.

Objective: To evaluate the impact on Electroencephalography (EEG) classification of different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We compared three attention-enhanced DL models, the brand-new InstaGATs, an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal (i.e., artifactual or pathological) EEG patterns. Results: We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. We could also prove that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the dataset. Conclusions: with this work, we shed light over the role of different attention mechanisms in the classification of normal and abnormal EEG patterns. Moreover, we discussed how they can exploit the intrinsic relationships in the temporal, frequency and spatial domains of our brain activity. Significance: Attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance, in different real-world scenarios. Moreover, it can make it easier to parallelize the computation and, thus, to speed up the analysis of big electrophysiological (e.g., EEG) datasets.

Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level cognitive and sensory functions. However, decoding PAC is still challenging. This contribution presents REPAC, a reliable and robust algorithm for modeling and detecting PAC events in EEG signals. First, we explain the synthesis of PAC-like EEG signals, with special attention to the most critical parameters that characterize PAC, i.e., SNR, modulation index, duration of coupling. Second, REPAC is introduced in detail. We use computer simulations to generate a set of random PAC-like EEG signals and test the performance of REPAC with regard to a baseline method. REPAC is shown to outperform the baseline method even with realistic values of SNR, e.g., -10 dB. They both reach accuracy levels around 99%, but REPAC leads to a significant improvement of sensitivity, from 20.11% to 65.21%, with comparable specificity (around 99%). REPAC is also applied to a real EEG signal showing preliminary encouraging results.

The classification of different fine hand movements from EEG signals represents a relevant research challenge, e.g., in brain-computer interface applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed convolutional neural network (CNN), and we compared its classification performance into respect to two well-established machine learning models, namely, a shrinked-LDA and a Random Forest. Compared to previous literature, we took advantage of the knowledge of the neuroscience field, and we trained our CNN model on the so-called Movement Related Cortical Potentials (MRCPs)s. They are EEG amplitude modulations at low frequencies, i.e., (0.3, 3) Hz, that have been proved to encode several properties of the movements, e.g., type of grasp, force level and speed. We showed that CNN achieved good performance in both datasets and they were similar or superior to the baseline models. Also, compared to the baseline, our CNN requires a lighter and faster pre-processing procedure, paving the way for its possible use in an online modality, e.g., for many brain-computer interface applications.

Pervasive healthcare is a promising assisted-living solution for chronic patients. However, current cutting-edge communication technologies are not able to strictly meet the requirements of these applications, especially in the case of life-threatening events. To bridge this gap, this paper proposes a new architecture to support indoor healthcare monitoring, with a focus on epileptic patients. Several novel elements are introduced. The first element is the cascading of a WLAN and a cellular network, where IEEE 802.11ax is used for the wireless local area network to collect physiological and environmental data in-home and 5G-enabled Fixed Wireless Access links transfer them to a remote hospital. The second element is the extension of the network slicing concept to the WLAN, and the introduction of two new slice types to support both regular monitoring and emergency handling. Moreover, the inclusion of local computing capabilities at the WLAN router, together with a mobile edge computing resource, represents a further architectural enhancement. Local computation is required to trigger not only health-related alarms, but also the network slicing change in case of emergency: in fact, proper radio resource scheduling is necessary for the cascaded networks to handle healthcare traffic together with other promiscuous everyday communication services. Numerical results demonstrate the effectiveness of the proposed approach while highlighting the performance gain achieved with respect to baseline solutions.

This paper discusses the potential of the Internet of Things (IoT) paradigm in the context of assisted living for elderly and fragile people, in the light of the peculiar requirements of such users, both from a functional and a technological perspective. We stress some aspects that are often disregarded by the technical community, such as technology acceptability and usability, and we describe the framework and the phases of the current co-design approaches that imply the active involvement of the final users in the system design process. Thereby, we identify a series of design practices to merge technical and fragile people's requirements. The discussion is backed up by the description of DOMHO, a prototypal IoT-based AAL system that embodies most of the concepts described in the paper, and that is being deployed and tested in a shelter house for elders, and in an apartment for the co-housing of individuals with disabilities. Finally, we discuss the potential and limits of the current approaches and present some open challenges and future research directions.

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance.

Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e.g., instrumented walkway). In this study, we present a novel method based on dilated convolutions for an accurate detection of gait events (initial and final foot contacts) from wearable inertial sensors. A rich dataset has been used to validate the method, featuring 71 people with Parkinson's disease (PD) and 67 healthy control subjects. Multiple sensors have been considered, one located on the fifth lumbar vertebrae and two on the ankles. The aims of this study were: (i) to apply deep learning (DL) techniques on wearable sensor data for gait segmentation and quantification in older adults and in people with PD; (ii) to validate the proposed technique for measuring gait against traditional gold standard laboratory reference and a widely used algorithm based on wavelet transforms (WT); (iii) to assess the performance of DL methods in assessing high-level gait characteristics, with focus on stride, stance and swing related features. The results showed a high reliability of the proposed approach, which achieves temporal errors considerably smaller than WT, in particular for the detection of final contacts, with an inter-quartile range below 70 ms in the worst case. This study showes encouraging results, and paves the road for further research, addressing the effectiveness and the generalization of data-driven learning systems for accurate event detection in challenging conditions.

The fifth generation (5G) of communication systems has ambitious targets of data rate, end-to-end latency, and connection availability, while the deployment of a new flexible network architecture will spur new applications. E-health and mobile health (m-health) solutions will meet the increasing demand of new, sustainable, and more accessible services beneficial to both practitioners and the rapidly aging population. This paper aims at defining the technical requirements of future cellular networks to support a variety of advanced healthcare services (e.g., smart hospital, telesurgery, connected ambulances, and monitoring). While 5G will be able to satisfy these requirements, it will also pave the way for future e- and m-health in the sixth-generation (6G) cellular networks.

In the context of a Brain Computer Interface platform implemented for the arm rehabilitation of mildly impaired stroke patients, two methods of EEG signals processing are compared in terms of (i) their identification performance rate and (ii) their computational complexity with the overall goal to select the most efficient and feasible real-time procedure. An effective signal processing is, indeed, one of the most critical issue for such kind of technology which aims to establish a real-time communication between the subject's brain and a machine, i.e. a computer, a robotic arm or another device, that should implement his/her intention to move in place of his/her impaired arm.

This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0:8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.

The aim of this study is to investigate bursts- related EEG signals in a focal hand dystonia patient. Despite of considering time domain and frequency domain techniques as mutually exclusive analysis, in this contribution we have taken advantage from both of them: particularly, in the frequency domain, coherence was used to identify the most likely frequency bands of interaction between brain and muscles, then, in the time domain, cross-correlation was exploited to verify the physiological reliability of such a relationship in terms of signal transmission delay from the centre to the periphery. Our preliminary results suggest - in line with recent literature - that activity in the high beta band (around 30 Hz) could represent an electroencephalographic correlate for the pathological electromyographic bursts affecting the focal hand dystonia condition. Even though a future study on a larger sample is needed to statistically support these preliminary findings, this contribution allows to think of new kinds of rehabilitation from focal hand dystonia that could target the actual electroencephalographic correlate of the pathology, i.e. phenotypically expressed by bursts, with the consequence of a relevant functional improvement., Comment: 5 pages, 6 figures. Submitted and accepted at Global Wireless Summit (GWS) 2016 at Aarhus University, Denmark. http://www.riverpublishers.com/search_ebook.php?val=gws&type=research&Search=Search, Proceedings of the Global Wireless Summit 2016. Editor: Peter Lindgren, Aarhus University, Denmark. e-ISBN: 9788793609297