Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System

The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation

Multimodal Feedback in Assisting a Wearable Brain-Computer Interface Based on Motor Imagery

A multimodal sensory feedback was exploited in the present study to improve the detection of neurological phenomena associated with motor imagery. At this aim, visual and haptic feedback were simultaneously delivered to the user of a brain-computer interface. The motor imagery-based brain-computer interface was built by using a wearable and portable electroencephalograph with only eight dry electrodes, a haptic suit, and a purposely implemented virtual reality application. Preliminary experiments were carried out with six subjects participating in five sessions on different days. The subjects were randomly divided into “control group” and “neurofeedback group”. The former performed pure motor imagery without receiving any feedback, while the latter received multimodal feedback as a response to their imaginative act. Results of a cross validation showed that at most 61% of classification accuracy was achieved in performing the pure motor imagination. On the contrary, subjects of the “neurofeedback group” achieved up to 82% mean accuracy, with a peak of 91% in one of the sessions. However, no improvement in pure motor imagery was observed, either when practicing with pure motor imagery or with feedback

Mindfulness-based Emotional Acceptance in Combination with Neurofeedback for Improving Emotion Self-Regulation: a Pilot Study

A feasibility of Mindfulness-based emotional acceptance in combination with neurofeedback for improving emotion self-regulation is presented. This represents, to our knowledge, an unexplored approach in the emotion regulation literature. The study was performed using a low-cost wearable system designed to perform electroencephalography (EEG) outside the clinical setting. The focused EEG feature is the beta-band Power Spectral Density along the midline (FCz-CPz electrodes). Four subjects new to the practice of mindfulness were involved in the experiments. A comparison between two neurofeedback conditions (in a within-subject design) is performed: a) cognitive reappraisal task; b) emotional acceptance task. In both cases the expected decrement of power spectral density in high-beta band linked to the neurofeedback training was found. Emotional acceptance in combination with neurofeedback emerged as a promising emotional regulation strategy

Sindrome fibromialgica : un caso clinico

La Sindrome Fibromialgica (SFM) \ue8 una malattia che colpisce i muscoli causando tensione muscolare e che si manifesta principalmente con le seguenti sensazioni: iperalgesia, rigidit\ue0, astenia e affaticamento. La diagnosi clinica non \ue8 sempre agevole poich\ue9 non esistono indagini di laboratorio che permettano l\u2019identificazione certa della malattia. Gli studi di neuro-imaging mostrano che la SFM sia legata ad una disfunzione cerebrale che non permette la corretta elaborazione del dolore, tuttavia non \ue8 chiaro se questa disfunzione ne sia causa o effetto. \uc8 indubbio per\uf2 che i fattori psicologici influiscano in maniera significativa sulla sintomatologia dolorosa. Il caso clinico proposto \ue8 riferito ad una paziente (A.) affetta da SFM inviata presso il Servizio di Psicoterapia del Dipartimento di Salute Mentale, Fondazione IRCSS Ca\u2019 Granda Ospedale Maggiore Policlinico di Milano. La paziente effettua una valutazione clinico-diagnostica al baseline (T0) che prevede la somministrazione della seguente batteria testale: Interviste Cliniche Semi-Strutturate per diagnosi secondo DSM-IV TR (SCID I e II), Hamilton Rating scale for Depression and Anxiety (HAM-D e HAM-A), Tema Relazionale Conflittuale Centrale (CCRT), Toronto Alexithymia Scale (TAS-20), Symptom Checklist (SCL-90-R) ed Inventory of Interpersonal Problems (IIP-127). Tale valutazione ha evidenziato un quadro psicopatologico caratterizzato da incapacit\ue0 di individuare e mentalizzare i propri stati emotivi e tendenza alla somatizzazione. In considerazione di quanto emerso e dei dati anamnestici della paziente, viene proposta una psicoterapia psicodinamica breve (STPP), della durata di 9 mesi, finalizzata al conseguimento di una maggiore consapevolezza di s\ue8 e delle proprie emozioni attraverso un focus centrato sul miglioramento delle relazioni interpersonali. Al termine del percorso psicoterapico la paziente esegue una valutazione follow-up (T1) con la medesima batteria testale utilizzata al T0. Le valutazioni al T1 non mostrano variazioni psicopatologiche clinicamente significative, tuttavia la paziente riferisce un vissuto soggettivo positivo, riportando la percezione di un miglioramento della sintomatologia algica cronica derivante da una maggior individuazione delle proprie sensazioni somatiche. Verranno illustrati brani delle sedute psicoterapeutiche per approfondire le principali tematiche cliniche

Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study

Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease

Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning

Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP), combining autonomous insulin delivery and blood glucose monitoring, is a promising solution. However, state-of-the-art APs require several information for bolus delivery, such as the estimated carbohydrate intake over the meals. This is mainly related to the limited knowledge of the determinants of PGR. Although meal carbohydrates are mostly considered as the major factor into, uencing PGR, other food components play a relevant role in PGRs, and thus, should be taken into account. Based on these considerations, a study to determine the effect of nutritional factors (i.e., carbohydrates, proteins, lipids, fibers, and energy intake) in the short and middle term on Blood Glucose Levels (BGLs) prediction was conducted by Machine Learning (ML) methods. A ML model able to predict the BGLs after 15, 30, 45, and 60 minutes from the meal leveraging on insulin doses, blood glucose, and nutritional factors in T1D patients on AP systems was implemented. More specifically, to investigate the impact of the nutritional factors on the model predictions, a Feed-Forward Neural Network, was fed with several dispositions of BGLs, insulin, and nutritional factors. Both public and self-produced data were used to validate the proposal. The results suggest that patient-specific information about nutritional factors can be significant for middle term postprandial BGLs predictions

Reproducible Assessment of Valence and Arousal Based on an EEG Wearable Device

An electroencephalography-based detection system of emotional states exploiting few dry channels is proposed. The circumplex model of affect was the reference theory adopted and the standardized dataset International Affective Picture System IAPS was exploited for emotion elicitation. A subset of stimuli polarized on both the valence and the arousal dimension was employed to maximize the effectiveness of the emotion induction. A Self-Assessment Manikin (SAM) was submitted to the subjects after each image to assess the valence and arousal scores of the target emotion. The agreement between the two measures, namely the IAPS scores and the SAM scores was verified through a Bland Altman analysis and a Spearman correlation analysis. An initial screening of the sample allowed to manage the bias caused by depressive and anxiety disorders. The proposed system was experimentally validated. 9 healthy subjects participated in the experimental activity and their EEG signals were acquired through an 8-channel headset. As a result, the best accuracy in the within-subject case of 62.5 ± 4.89 % for the valence dimension and of 66.67 ± 11.88 % for the arousal dimension, was obtained. The poor correlation emerged between IAPS scores and SAM scores negatively impacts on the accuracy and highlights the issue of IAPS update

Neural Network-Based Prediction and Monitoring of Blood Glucose Response to Nutritional Factors in Type-1 Diabetes

Type 1 diabetes (T1D) is an autoimmune disease that affects millions of people worldwide. A most challenging aspect regarding diabetes therapy is the way to calculate the insulin bolus amount to inject before meals. The artificial pancreas (AP), combining both blood glucose monitoring and automatic insulin delivery, has demonstrated its effectiveness in T1D treatment. However, one of the limitations of current AP devices is the fact that the patient needs to insert manually the amount of insulin to be released and the bolus is calculated on the estimated carbohydrate intake, while other nutritional factors of the patient's meal are not taken into account. To overcome this issue, in this paper, two innovative algorithms to predict the postprandial blood glucose concentration after the meal in T1D patients on AP systems are presented. The proposed algorithms, which cover a time span of prediction of 180 minutes, take into account not only the carbohydrates amount in the meal but also other selected nutritional factors. More specifically, the proposed algorithms are based on feed forward multi-layer neural networks (FFNNs) and long short-term memory networks (LSTMNs) with a specific hyper-parameter configuration. The output of the proposed architectures consists of a predicted glycemic curve. The algorithms were validated by comparing the predictions of the networks performed on a test dataset with the measured glycemic values recorded by the hybrid closed-loop systems worn by the patients

Virtual Reality Enhances EEG-Based Neurofeedback for Emotional Self-regulation

A pilot study to investigate possible differences between a virtual reality-based neurofeedback and a traditional neurofeedback is presented. Neurofeedback training aimed to strengthen the emotional regulation capacity. The neurofeedback task is to down-regulate negative emotions by decreasing the beta band power measured in the midline areas of the scalp (i.e., Fcz-Cpz). Negative International Affective Picture System images were chosen as eliciting stimuli. Three healthy subjects participated in the experimental activities. Each of them underwent three VR-based neurofeedback sessions and three neurofeedback sessions delivered on a traditional 2D screen. The neurofeedback training session was preceded by a calibration phase allowing to record the rest and the baseline values to adapt the neurofeedback system to the user. For the majority of sessions, the average value of the high beta band power during the neurofeedback training remained below the baseline, as expected. In compliance with previous studies, future works should investigate the virtual reality-based neurofeedback efficacy in physiological responses and behavioral performance

Evaluation of the Automated QIAsymphony SP/AS Workflow for Cytomegalovirus DNA Extraction and Amplification from Dried Blood Spots

Human cytomegalovirus (CMV) can be considered the most important agent of congenital infection. Long-term sequelae of congenital infection occur in about 15% of infants asymptomatic at birth. To avoid long-term sequelae or to reduce their burden, it is necessary to identify infected children for early interventions. CMV DNA can be detected in dried blood spots (DBSs). DBSs have been used in several studies for the retrospective diagnosis of congenital CMV (CCMV). It has been proposed to use DBSs for the newborn screening of CMV infection; however, manual methods are not suitable for newborn screening of CCMV