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Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) are vital for various applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery (MI) tasks, remains a significant challenge. This study introduces a novel Weighted and Stacked Adaptive Integrated Ensemble Classifier (WS-AIEC), employing a comprehensive approach across six MI EEG datasets with 16 diverse Machine Learning (ML) classifiers. Through evaluations that encompass metric-based comparisons and learning curve analyses, we systematically ranked and clustered the classifiers. The WS-AIEC integrates the top-performing classifiers from each cluster and employs a unique blend of weighted and stacked ensemble techniques. Our results demonstrate the WS-AIEC’s superior performance, achieving an exceptional accuracy of 99.58% on the BNCI2014-002 dataset and an average improvement of 20.23% in accuracy over the top-performing individual classifiers across all datasets. This significant enhancement underscores the innovative approach of our WS-AIEC in EEG signal classification for BCIs, setting a new benchmark for accuracy and reliability in the field.

The firing instants of single motor units (MUs) can be identified by decomposing electromyograms (EMG) detected with intramuscular or grids of surface electrodes. The latter is sometimes preferred due to its larger detection volume and non-invasiveness. When the interest is in firing instants and not in investigating the activity of specific MUs, in-silico studies have shown that deconvolution of a single surface EMG is a low cost method providing reliable information. In this study, we explored this possibility by testing the experimental validity of deconvolution by comparison with decomposition of multichannel surface EMG. A single kernel deconvolution method is proposed to estimate the cumulative firings of the MUs from bipolar surface EMGs collected from the biceps brachii of 10 healthy subjects, recorded during contractions of different force levels and an endurance test. Different parameters were tested: force levels, inter-electrode distance, electrode size and location. Validity was assessed by correlating the cumulative firings (after 5–45 Hz band-pass filtering) between the proposed, deconvolution approach and the already validated, EMG decomposition. For all conditions tested, decomposition and deconvolution provided correlation coefficients of about 40%. When considering experimental signals reconstructed with the firings of decomposed MUs, markedly higher correlation values were obtained (median correlations of 90%). High correlation (about 80%) was obtained even when a signal with large interference was built by adding about 90 MU action potential trains, decomposed from different EMGs of our dataset with same contraction levels. Analysis of residual root mean squared error (median across tests of about 40% and 15% for decomposition and deconvolution, respectively) together with the good estimation on reconstructed signals with high interference suggest that deconvolution may identify additional contributions that are not explained by decomposition. This additional information provided by deconvolution may justify in part the discrepancy when comparing the outputs of the two methods applied to the original signals. The cumulative firing instants associated with action potentials can be accurately estimated with the deconvolution of a single bipolar surface EMG.

In the rapidly evolving domain of brain-to-brain communication, safeguarding the transmission of information against adversarial threats is paramount. This study introduces an advanced approach to enhance the resilience and security of brain-to-brain communication systems utilizing electroencephalogram data against such threats through adversarial neural network training. Concentrating on event-related potentials and employing a diverse collection of eight datasets, our research rigorously evaluates and optimizes the system’s defense mechanisms against adversarial manipulations. We specifically target the optimization of trial durations and sampling rates to bolster system security. Our findings reveal a marked improvement in the system’s defensive capabilities, demonstrated by a significant increase in adversarial accuracy by 17% and enhancement in the area under the receiver operating characteristic curve by 0.12 points. These results underscore the efficacy of our approach in fortifying brain-to-brain communication systems against sophisticated cyber threats, marking a significant step forward in the secure and robust transmission of neural signals.

Recent years have seen progress in the intersection of computer science and biomedicine, progress that has led to significant advancements in healthcare diagnostics, treatment, and patient care [...]

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.

Objective: Crosstalk in surface electromyogram (EMG) is an important open problem and the common strategy of reducing it through spatial filters needs improvements. Methods: We evaluated experimentally the optimal spatio-temporal filter (OSTF), i.e., a recent approach that adapts to the subject, filtering different EMG channels both in time and space to emphasize the signal of a target muscle discarding that of adjacent ones. EMGs were recorded by a high-density recording system from pronator teres (target muscle) and flexor carpi radialis (crosstalk muscle) of 8 healthy subjects. OSTF was tested in different conditions, considering one channel per muscle (either single or double differential, SD and DD, respectively), changing the selectivity of detection (small electrodes close to each other, or large ones with higher inter-electrode distance), the force applied by the muscles (whose EMGs were summed to simulate different levels of crosstalk), and the duration of the signal to train the method. Results: OSTF was less affected by crosstalk than SD and DD filters. Statistically significant improvements were obtained in reducing the crosstalk-induced variations: for example, considering small electrodes, we obtained a percentage error of 157.30±57.11 % and 38.54±10.47 % (mean±std) in the estimation of the average rectified value (ARV), and an error of 23.57±3.92 % and 8.31±0.88 % in the estimation of the median frequency (MDF), for SD and OSTF, respectively. Conclusion: The OSTF can be applied in real-time, is easy to use, and is feasible even when using only few detection channels, as is customary in many applications.

Abstract The influence of large vessels on near infrared spectroscopy (NIRS) measurement is generally considered negligible. Aim of this study is to test the hypothesis that changes in the vessel size, by varying the amount of absorbed NIR light, could profoundly affect NIRS blood volume indexes. Changes in haemoglobin concentration (tHb) and in tissue haemoglobin index (THI) were monitored over the basilic vein (BV) and over the biceps muscle belly, in 11 subjects (7 M – 4 F; age 31 ± 8 year) with simultaneous ultrasound monitoring of BV size. The arm was subjected to venous occlusion, according to two pressure profiles: slow (from 0 to 60 mmHg in 135 s) and rapid (0 to 40 mmHg maintained for 30 s). Both tHb and THI detected a larger blood volume increase (1.7 to 4 fold; p

The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABAA, AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0–48 h) and the α-syn concentration (0.3–70 μM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC50 = 1.03 μM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient’s hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.

Assessment of vascular size and of its phasic changes by ultrasound is important for the management of many clinical conditions. For example, a dilated and stiff inferior vena cava reflects increased intravascular volume and identifies patients with heart failure at greater risk of an early death. However, lack of standardization and sub-optimal intra- and inter- operator reproducibility limit the use of these techniques. To overcome these limitations, we developed two image-processing algorithms that quantify phasic vascular deformation by tracking wall movements, either in long or in short axis. Prospective studies will verify the clinical applicability and utility of these methods in different settings, vessels and medical conditions.

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain–machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users’ needs are considered.

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Interference surface electromyogram (EMG) recorded from linear electrode arrays aligned to muscle fibres can be separated into propagating and non-propagating contributions. The first reflects the propagation of action potentials along muscle fibres. The non-propagating components are here shown to be related to the overall firing pattern of active motor units (MU). Indeed, in simulations, the power spectral density (PSD) of non-propagating components shows a low frequency peak corresponding to the mean firing rate, even when such a contribution is not visible in the PSD of the EMG (either monopolar or single differential configuration, either rectified or not). Moreover, it has a high correlation with the PSD of the cumulative firings of the MUs in the detection volume of the recording system. Applications to experimental data confirm that the low frequency peak is more evident for the non-propagating components than for the raw signals and is related to the MU firing frequency. Potential future applications are expected in the study of the MU control in different conditions (e.g., training, fatigue or pathology, inducing changes, or modulation of firing rate) and in the investigation of common synaptic inputs to motor neurons.

Chiari I malformation is characterized by the herniation of cerebellar tonsils below the foramen magnum. It is often accompanied by syringomyelia and neurosurgical management is still controversial. In fact, it is frequent that some symptomatic patients initially undergo bony decompression of the posterior fossa and need in a short time more invasive surgery with higher morbility (e.g., decompression of posterior fossa with dural plastic, with or without tonsillar coarctation) because of unsatisfactory results at MRI controls. This study proposes a machine learning approach (based on SVM classifier), applied to different morphometric indices estimated from sagittal MRI and some information on the patient (i.e., age and symptoms at diagnosis), to recognize patients with higher risk of syringomyelia and clinical deterioration. Our database includes 58 pediatric patients who underwent surgery treatment. A negative outcome at 1 year from the intervention was observed in 38% of them (accuracy of 62%). Our algorithm allows us to increase the accuracy to about 71%, showing it to be a valid support to neurosurgeons in refining the clinical picture.

Aortic stiffness is an important clinical parameter for predicting cardiovascular events. Carotid–femoral pulse wave velocity (cf-PWV) has been proposed for performing this evaluation non-invasively; however, it requires dedicated equipment and experienced operators. We explored the possibility of measuring aortic stiffness using ultrasound scans of the abdominal aorta coupled with the Bramwell–Hill equation. Healthy subjects were investigated; measurements of cf-PWV were taken by arterial tonometry and aortic systo-diastolic pressure difference was estimated using a validated model. Pulsatility of an abdominal tract of aorta was assessed by automated processing of ultrasound scans. Through a Bland–Altmann analysis, we found large biases when estimating each parameter by applying the Bramwell–Hill equation to the measured values of the other two paramters (bias, ± 1.96 SD; PWV, about 2.1 ± 2.5 m/s; pulsatility, 12 ± 14%; pressure jump, 47 ± 55 mmHg). These results indicate that the two measures are not interchangeable, and that a large part of the bias is attributable to blood pressure estimation. Further studies are needed to identify the possible sources of bias between cf-PWV and aortic pulsatility.

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

Ultrasound (US)-based measurements of the inferior vena cava (IVC) diameter are widely used to estimate right atrial pressure (RAP) in a variety of clinical settings. However, the correlation with invasively measured RAP along with the reproducibility of US-based IVC measurements is modest at best. In the present manuscript, we discuss the limitations of the current technique to estimate RAP through IVC US assessment and present a new promising tool developed by our research group, the automated IVC edge-to-edge tracking system, which has the potential to improve RAP assessment by transforming the current categorical classification (low, normal, high RAP) in a continuous and precise RAP estimation technique. Finally, we critically evaluate all the clinical settings in which this new tool could improve current practice.

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial.

Non-linear analysis found many applications in biomedicine. Approximate entropy (ApEn) is a popular index of complexity often applied to biomedical data, as it provides quite stable indications when processing short and noisy epochs. However, ApEn strongly depends on parameters, which were chosen in the literature in wide ranges. This paper points out that ApEn depends on sampling rate of continuous time signals, embedding dimension, tolerance (under which a match is identified), epoch duration and low frequency trends. Moreover, contradicting results can be obtained changing parameters. This was found both in simulations and in experimental EEG. These limitations of ApEn suggest the introduction of an alternative index, here called modified ApEn, which is based on the following principles: oversampling is compensated, self-recurrences are ignored, a fixed percentage of recurrences is selected and low frequency trends are removed. The modified index allows to get more stable measurements of the complexity of the tested simulated data and EEG. The final conclusions are that, in order to get a reliable estimation of complexity using ApEn, parameters should be chosen within specific ranges, data must be sampled close to the Nyquist limit and low frequency trends should be removed. Following these indications, different studies could be more easily compared, interpreted and replicated. Moreover, the modified ApEn can be an interesting alternative, which extends the range of parameters for which stable indications can be achieved.

Using computerized pupillometry, our previous research established that the autonomic nervous system (ANS) is dysregulated in patients suffering from temporomandibular disorders (TMDs), suggesting a potential role for ANS dysfunction in pain modulation and the etiology of TMD. However, pain modulation hypotheses for TMD are still lacking. The periaqueductal gray (PAG) is involved in the descending modulation of defensive behavior and pain through μ, κ, and δ opioid receptors. Transcutaneous electrical nerve stimulation (TENS) has been extensively used for pain relief, as low-frequency stimulation can activate µ receptors. Our aim was to use pupillometry to evaluate the effect of low-frequency TENS stimulation of μ receptors on opioid descending pathways in TMD patients. In accordance with the Research Diagnostic Criteria for TMD, 18 females with myogenous TMD and 18 matched-controls were enrolled. All subjects underwent subsequent pupillometric evaluations under dark and light conditions before, soon after (end of stimulation) and long after (recovery period) sensorial TENS. The overall statistics derived from the darkness condition revealed no significant differences in pupil size between cases and controls; indeed, TENS stimulation significantly reduced pupil size in both groups. Controls, but not TMD patients, displayed significant differences in pupil size before compared with after TENS. Under light conditions, TMD patients presented a smaller pupil size compared with controls; the pupil size was reduced only in the controls. Pupil size differences were found before and during TENS and before and after TENS in the controls only. Pupillometry revealed that stimulating the descending opioid pathway with low-frequency sensory TENS of the fifth and seventh pairs of cranial nerves affects the peripheral target. The TMD patients exhibited a different pattern of response to TENS stimulation compared with the controls, suggesting that impaired modulation of the descending pain system may be involved in TMD.

The role of the autonomic nervous system (ANS) was recently investigated in Temporomandibular disorders (TMD). Several authors argue that in subjects with TMD there is a dysregulation of ANS. Recent literature support that Pupillometry is a simple non-invasive tool to study ANS. The aim of this study was to investigate the relationship between TMD and ANS activity using pupillometry recording in Infrared light at rest Mandible Position (RP); Infrared light at Forced Habitual Occlusion (FHO); Yellow-green light at RP; Yellow-green light at FHO. Forty female subjects were enrolled: 20 case patients showed TMD based on the Research Diagnostic Criteria for TMD, and 20 control patients, aged matched, had no signs or symptoms of TMD. Statistical analysis was performed on average pupil size. Ratio between pupil size in FHO and RP (FHO/RP ratio) and yellow-green and infrared (light/darkness ratio) lighting were carried out. Within group differences of pupil size and of "ratio" were analyzed using a paired t test, while differences of pupil size between groups were tested using an unpaired t test. Statistical comparisons between groups showed no significant differences of absolute values of pupil dimension in RP and FHO, both in yellow-green and in infrared lighting. In addition, there were no significant differences within groups comparing RP and FHO in yellow-green light. In within group comparison of pupil size, differences between RP and FHO were significant in infrared conditions. Control subjects increased, whereas TMD patients decreased pupil size at FHO in infrared lightening. FHO/RP ratio in darkness and light/darkness ratio in RP were significantly different between groups. Taken together, these data suggest that TMD subjects have an impairment of the sympathetic-adrenergic component of the ANS to be activated under stress. The present study provides preliminary pupillometric data confirming that adrenergic function is dysregulated in patients with TMD.