Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

A systematic review on machine-learning strategies for improving generalization in electroencephalographybased emotion classification was realized. In particular, cross-subject and cross-session generalization was focused. In this context, the non-stationarity of electroencephalographic (EEG) signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. In this review, 449 papers were retrieved from the Scopus, , IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 79 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject or cross-session validation strategy, or making use of other biosignals as support were excluded. On the basis of the selected papers' analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion of the different ML approaches involved. The studies reporting the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems

The Italian Society for Rheumatology recommendations for the management of axial spondyloarthritis

: Over the last few years, the landscape of treatments for axial spondyloarthritis (SpA) has been rapidly evolving, urging international scientific societies to draft or update existing clinical practice guidelines (CPGs) on the management of axial SpA. The Italian Society for Rheumatology (SIR) committed to provide revised and adapted evidence- and expert-based recommendations for the management of patients with axial SpA in Italy. A systematic approach to the adaptation of existing CPGs - the ADAPTE methodology - was adopted to obtain updated recommendations suitable for the Italian context. A systematic literature search was performed in Medline and Embase databases to find international CPGs and consensus statements with recommendations for the management of axial SpA published in the previous five years. A working group composed of rheumatologists with proven experience in the management of axial SpA and methodologists identified the key research questions which guided study selection and data extraction. Guideline quality was assessed using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. The Italian recommendations were developed by endorsing or adapting and rewording some existing recommendations. The draft of the recommendations was sent to a multidisciplinary group of external reviewers for comment and rating. Six original CPGs were selected and used to create this SIR CPG, which includes a final set of 14 recommendations covering the management of patients with axial SpA across the following domains: assessment, pharmacological and non-pharmacological treatment, and follow-up. The dissemination and implementation of these SIR recommendations are expected to support an evidencebased clinical approach to the management of patients with axial SpA in Italy

The Italian Society for Rheumatology clinical practice guidelines for rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disorder characterised by chronic joint inflammation, leading to functional disability and increased risk of premature death. Clinical practice guidelines (CPGs) are expected to play a key role in improving management of RA, across the different phases of the disease course. Since new evidence has become available, the Italian Society for Rheumatology (SIR) has been prompted to update the 2011 recommendations on management of RA.The framework of the Guidelines International Network Adaptation Working Group was adopted to identify, appraise (AGREE II), synthesize, and customize the existing RA CPGs to the Italian healthcare context. The task force consisting of rheumatologists from the SIR Epidemiology Research Unit and a committee with experience in RA identified key health questions to guide a systematic literature review. The target audience includes physicians and health professionals who manage RA in practice, and the target population includes adult patients diagnosed as having RA. An external multi-disciplinary committee rated the final version of the CPGs. From the systematic search in databases (Medline, Embase) and grey literature, 6 CPGs were selected and appraised by two independent raters. Combining evidence and statements from these CPGs and clinical expertise, 8 (Management) +6 (Safety) recommendations were developed and graded according to the level of evidence. The statements and potential impact on clinical practice were discussed and assessed.These revised recommendations are intended to provide guidance for the management of RA and to disseminate the best evidence-based clinical practices for this disease

The Urokinase/Urokinase Receptor System in Mast Cells: Effects of its Functional Interaction with fMLF Receptors.

Mast cell and basophils express the high affinity receptor for IgE (FcɛRI) and are primary effector cells of allergic disorders. The urokinase (uPA)-mediated plasminogen activation system is involved in physiological and pathological events based on cell migration and tissue remodelling, such as inflammation, wound healing, angiogenesis and metastasis. uPA is a serine protease that binds uPAR, a high affinity glycosyl-phosphatidyl-inositol (GPI)-anchored receptor. uPAR focuses uPA activity at the cell surface and activates intracellular signaling through lateral interactions with integrins, receptor tyrosine kinases and the G-protein-coupled family of fMLF chemotaxis receptors (FPRs). We investigated the expression of the uPA-uPAR system and its functional interaction with FPRs in human mast cells (MCs). Differently from basophils, MCs produced uPA that was able to induce their chemotaxis. Indeed, MCs also expressed uPAR, both in the intact and in a cleaved form (DII-DIII-uPAR) that can expose, at the N-terminus, the SRSRY sequence, able to interact with FPRs and to mediate cell chemotaxis. MCs also expressed mRNAs for FPRs that were functionally active; indeed, uPA and a soluble peptide (uPAR84-95), containing the SRSRY chemotactic sequence of uPAR and able to interact with FPRs, were able to induce MCs chemotaxis. Thus, uPA is a potent chemoattractant for MCs acting through the exposure of the chemotactic epitope of uPAR, that is an endogenous ligand for FPRs. The same mechanism could be involved in VEGF-A secretion by human MCs, also induced by uPA and uPAR84-95 stimulation

β2-Adrenergic receptor stimulation improves endothelial progenitor cell-mediated ischemic neoangiogenesis

Endothelial progenitor cells (EPCs) are present in the systemic circulation and home to sites of ischemic injury where they promote neoangiogenesis. β2-Adrenergic receptor (β2AR) plays a critical role in vascular tone regulation and neoangiogenesis