XAI approach for addressing the dataset shift problem: BCI as a case study

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions leading ML systems toward poor generalisation performances. Therefore, such systems can be unreliable and risky, particularly when used in safety-critical domains. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are used. In fact, EEG signals are highly non-stationary signals both over time and between different subjects. Despite several efforts in developing BCI systems to deal with different acquisition times or subjects, performance in many BCI applications remains low. Exploiting the knowledge from eXplainable Artificial Intelligence (XAI) methods can help develop EEG-based AI approaches, overcoming the performance returned by the current ones. The proposed framework will give greater robustness and reliability to BCI systems with respect to the current state of the art, alleviating the dataset shift problem and allowing a BCI system to be used by different subjects at different times without the need for further calibration/training stages

Biomechanically tunable nano-silica/p-hema structural hydrogels for bone scaffolding

Innovative tissue engineering biomimetic hydrogels based on hydrophilic polymers have been investigated for their physical and mechanical properties. 5% to 25% by volume loading PHEMA-nanosilica glassy hybrid samples were equilibrated at 37◦C in aqueous physiological isotonic and hypotonic saline solutions (0.15 and 0.05 M NaCl) simulating two limiting possible compositions of physiological extracellular fluids. The glassy and hydrated hybrid materials were characterized by both dynamo-mechanical properties and equilibrium absorptions in the two physiological-like aqueous solutions. The mechanical and morphological modifications occurring in the samples have been described. The 5% volume nanosilica loading hybrid nanocomposite composition showed mechanical characteristics in the dry and hydrated states that were comparable to those of cortical bone and articular cartilage, respectively, and then chosen for further sorption kinetics characterization. Sorption and swelling kinetics were monitored up to equilibrium. Changes in water activities and osmotic pressures in the water-hybrid systems equilibrated at the two limiting solute molarities of the physiological solutions have been related to the observed anomalous sorption modes using the Flory-Huggins interaction parameter approach. The bulk modulus of the dry and glassy PHEMA-5% nanosilica hybrid at 37◦C has been observed to be comparable with the values of the osmotic pressures generated from the sorption of isotonic and hypotonic solutions. The anomalous sorption modes and swelling rates are coherent with the difference between osmotic swelling pressures and hybrid glassy nano-composite bulk modulus: the lower the differences the higher the swelling rate and equilibrium solution uptakes. Bone tissue engineering benefits of the use of tuneable biomimetic scaffold biomaterials that can be “designed” to act as biocompatible and biomechanically active hybrid interfaces are discussed

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this record.OBJECTIVE: The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH: Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS: The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE: The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.The work presented in this paper was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241. URL: www.michelangelo-project.eu/

Enamel Erosion Reduction through Coupled Sodium Fluoride and Laser Treatments before Exposition in an Acid Environment: An In Vitro Randomized Control SEM Morphometric Analysis

(1) Background: Erosive lesions of dental enamel are steadily increasing owing to both the availability of exogenous acid and the production of endogenous acid. The aim of this study was to investigate the erosion-inhibiting potential of a diode laser irradiation and topical application of fluoride used alone or in combination on the enamel surface of extracted teeth before exposure to an acidic solution. (2) Methods: The four axial enamel surfaces of 40 healthy molars were used for four study groups: (A) no treatment; (B) application of fluoride gel for 120 s; (O) a diode laser application for 120 s; and (X) a combined laser/fluoride for 120 s. Each enamel surface was examined by SEM (scanning electron microscopy). (3) Results: At 700× magnification, it was possible to detect the enamel prisms of the test area of groups A, B, and O, while no structures such as enamel prisms were highlighted for group X because they were covered by an amorphous layer. The mean number of prisms ×1000 µm2 was 7.2 units with an SD of 0.72 for group A, 8 units with an SD of 0.96 for group B, and 4.8 units with a SD of 0.4 for group O. Student’s t-test showed no significant difference between group A and B with a p = 0.054. Group O showed a significant reduction of prims ×1000 µm2 compared with group A (p = 0.0027) and group B (p = 0.0009). Student’s t-test showed no significant difference between groups A and B with a p = 0.054. Group O showed a significant reduction of prims density with respect to group A (p = 0.0027) and group B (p = 0.0009). (4) Conclusions: This amorphous layer might be correlated with the effect of laser on enamel, which reduces both water and carbonate ion; increases the crystallinity of hydroxyapatite, and improves the mechanical properties of enamel; which is responsible for greater protection expressed by the enamel of group X against acid attacks

Some aspects of the human body's hydraulics

This paper presents some aspects related to the human body's hydraulics in the desire to make readers aware of how to maintain all the blood vessels of the human body in order to maintain the entire healthy, functional, young, vigorous circulatory system for a while the longest possible. The problem is complex because it has to be viewed from all points of view and not only as an isolated system in the body, having aspects of feedback on the whole physiopathology belonging to the human body. The highly circulating system needs permanent maintenance. Self-maintenance is done through various physiological mechanisms tightly linked to each other, including the lymphatic, digestive, renal, lung, nervous, glandular system… It is not possible to completely separate the physiology of a system from the other adjacent systems because they all work synergistically, being permanently controlled by the central and peripheral nervous system

Received cradling bias during the first year of life: A retrospective study on children with typical and atypical development

A population-level left cradling bias exists whereby 60-90% of mothers hold their infants on the left side. This left biased positioning appears to be mutually beneficial to both the mother and the baby’s brain organization for processing of socio-emotional stimuli. Previous research connected cradling asymmetries and Autism Spectrum Disorders (ASD), entailing impairment in socio- communicative relationships and characterized by an early hypo-lateralization of brain functions. In this explorative study, we aimed to provide a contribution to the retrospective investigations by looking for early behavioral markers of neurodevelopmental disorders such as ASD. We hypothesized that an atypical trajectory in maternal cradling might be one of the possible signs of an interference in mother-infant socio-emotional communication, and thus of potential neurodevelopmental dysfunctions. To this aim, we examined photos depicting mother-child early cradling interactions by consulting family albums of 27 children later diagnosed with autism and 63 typically developing children. As regards the first half of the first year of life, no differences were shown between maternal cradling-side preferences in typical and ASD groups, both exhibiting the left-cradling bias in the 0-3 months period, but not in the 3-6 months period. However, our results show dissimilar patterns of cradling preferences during the second half of the first year of life. In particular, the absence of left-cradling shown in typical mothers was not observed in ASD mothers, who exhibited a significant left-cradling bias in the 6-12 months age group. This difference might reflect the fact that mother-infant relationship involving children later diagnosed with ASD might remain “basic” because mothers experience a lack of social activity in such children. Alternatively, it may reflect the overstimulation in which mothers try to engage infants in response to their lack of responsiveness and social initiative. However, further investigations are needed both to distinguish between these two possibilities and to define the role of early typical and reversed cradling experiences on neurodevelopment

Biomechanically inspired shape memory effect machines driven by muscle like acting NiTi alloys

The research shows a bioinspired approach to be adopted to design of systems based on Shape Memory Alloys (SMAs), a class of Smart Materials that has in common with muscles the capability to react to an impulse (thermal for SMAs) with a contraction. The biomechanically inspired machine that is discussed in the paper refers to the antagonistic muscles pairs, which belongs to the Skeletal Muscles and are normally arranged in opposition so that as one group of muscles contract another group relaxes or lengthens. The study proposes a model, a solution not only to design a specific application, but also to provide an approach to be used for a wide range of adaptive applications (switchable windows, smart shadow systems, parking and urban shelters, etc.), where the shape changes in response to different external stimuli. The use of antagonist pairs mechanism provides a solution for better optimized systems based on SMAs where the main and proven advantages are: Easier and faster change of shape, lower need of energy for system operation, lower cost for SMA training and no problem of overheating