Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to using a single type of task, and the exploration of its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifaceted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks with a single-channel prefrontal EEG recording. Significant positive correlations were observed between the subjective flow scores of the individual’s best-flow-experience task and the EEG activities at the delta, gamma, and theta bands, peaking at latencies around 2 min after task onset. The outcomes of regression analysis yield a maximum R² of 0.163. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience. Full article

Postpartum depression (PPD) is a serious mental health issue among women after childbirth, and screening systems that incorporate questionnaires have been utilized to screen for PPD. These questionnaires are sensitive but less specific, and the additional use of objective measures could be helpful. The present study aimed to verify the usefulness of a measure of autonomic function, heart rate variability (HRV), which has been reported to be dysregulated in people with depression. Among 935 women who had experienced childbirth and completed the Edinburgh Postnatal Depression Scale (EPDS), HRV was measured in EPDS-positive women (n = 45) 1 to 4 weeks after childbirth using a wearable device. The measurement was based on a three-behavioral-state paradigm with a 5 min duration, consisting of rest (Rest), task load (Task), and rest-after-task (After) states, and the low-frequency power (LF), the high-frequency power (HF), and their ratio (LF/HF) were calculated. Among the women included in this study, 12 were diagnosed with PPD and 33 were diagnosed with adjustment disorder (AJD). Women with PPD showed a lack of adequate HRV regulation in response to the task load, accompanying a high LF/HF score in the Rest state. On the other hand, women with AJD exhibited high HF and reduced LF/HF during the After state. A linear discriminant analysis using HRV indices and heart rate (HR) revealed that both the differentiation of PPD and AJD patients from the controls and that of PPD patients from AJD patients were possible. The sensitivity and specificity for PPD vs. AJD were 75.0% and 90.9%, respectively. Using this paradigm, an HRV measurement revealed the characteristic autonomic profiles of PPD and AJD, suggesting that it may serve as a point-of-care sensing tool in PPD screening systems. Full article

Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK’s layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation. Full article

Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts. Full article

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by $\mathrm{67\%}$ more than rigid-attached sensors when the window size is $0.5$$\mathrm{s}$. Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured. Full article

Different visual stimuli can capture and shift attention into different directions. Few studies have explored differences in brain response due to directional (DS) and non-directional visual stimuli (nDS). To explore the latter, event-related potentials (ERP) and contingent negative variation (CNV) during a visuomotor task were evaluated in 19 adults. To examine the relation between task performance and ERPs, the participants were divided into faster (F) and slower (S) groups based on their reaction times (RTs). Moreover, to reveal ERP modulation within the same subject, each recording from the single participants was subdivided into F and S trials based on the specific RT. ERP latencies were analysed between conditions ((DS, nDS); (F, S subjects); (F, S trials)). Correlation was analysed between CNV and RTs. Our results reveal that the ERPs’ late components are modulated differently by DS and nDS conditions in terms of amplitude and location. Differences in ERP amplitude, location and latency, were also found according to subjects’ performance, i.e., between F and S subjects and trials. In addition, results show that the CNV slope is modulated by the directionality of the stimulus and contributes to motor performance. A better understanding of brain dynamics through ERPs could be useful to explain brain states in healthy subjects and to support diagnoses and personalized rehabilitation in patients with neurological diseases. Full article

When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation. Full article

Sustained involuntary muscle activity (IMA) is a highly disabling phenomenon that arises in the acute phase of an upper motor neuron lesion (UMNL). Wearable probes for long-lasting surface EMG (sEMG) recordings have been recently recommended to detect IMA insurgence and to quantify its evolution over time, in conjunction with a complex algorithm for IMA automatic identification and classification. In this study, we computed sensitivity (Se), specificity (Sp), and overall accuracy (Acc) of this algorithm by comparing it with the classification provided by two expert assessors. Based on sample size estimation, 6020 10 s-long sEMG epochs were classified by both the algorithm and the assessors. Epochs were randomly extracted from long-lasting sEMG signals collected in-field from 14 biceps brachii (BB) muscles of 10 patients (5F, age range 50–71 years) hospitalized in an acute rehabilitation ward following a stroke or a post-anoxic coma and complete upper limb (UL) paralysis. Among the 14 BB muscles assessed, Se was 85.6% (83.6–87.4%); Sp was 89.7% (88.6–90.7%), and overall Acc was 88.5% (87.6–89.4%) and ranged between 78.6% and 98.7%. The presence of IMA was detected correctly in all patients. These results support the algorithm’s use for in-field IMA assessment based on data acquired with wearable sensors. The assessment and monitoring of IMA in acute and subacute patients with UMNL could improve the quality of care needed by triggering early treatments to lessen long-term complications. Full article

Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system. Full article

There is a debate about whether abstract semantics could be represented in a motor domain as concrete language. A contextual association with a motor schema (action or gesture) seems crucial to highlighting the motor system involvement. The present study with transcranial magnetic stimulation aimed to assess motor cortex excitability changes during abstract word comprehension after conditioning word reading to a gesture execution with congruent or incongruent meaning. Twelve healthy volunteers were engaged in a lexical-decision task responding to abstract words or meaningless verbal stimuli. Motor cortex (M1) excitability was measured at different after-stimulus intervals (100, 250, or 500 ms) before and after an associative-learning training where the execution of the gesture followed word processing. Results showed a significant post-training decrease in hand motor evoked potentials at an early processing stage (100 ms) in correspondence to words congruent with the gestures presented during the training. We hypothesized that traces of individual semantic memory, combined with training effects, induced M1 inhibition due to the redundancy of evoked motor representation. No modulation of cortical excitability was found for meaningless or incongruent words. We discuss data considering the possible implications in research to understand the neural basis of language development and language rehabilitation protocols. Full article

Prior studies indicate differences in brain volume and neurophysiological responses of musicians relative to non-musicians. These differences are observed in the sensory, motor, parietal, and frontal cortex. Children with a fetal alcohol spectrum disorder (FASD) experience deficits in auditory, motor, and executive function domains. Therefore, we hypothesized that short-term music training in children with an FASD due to prenatal alcohol exposure may improve brain function. Children (N = 20) with an FASD were randomized to participate in either five weeks of piano training or to a control group. Selective attention was evaluated approximately seven weeks apart (pre-/post-music training or control intervention), examining longitudinal effects using the Attention Networks Test (ANT), a well-established paradigm designed to evaluate attention and inhibitory control, while recording EEG. There was a significant group by pre-/post-intervention interaction for the P250 ms peak of the event-related potential and for theta (4–7 Hz) power in the 100–300 ms time window in response to the congruent condition when the flanking stimuli were oriented congruently with the central target stimulus in fronto-central midline channels from Cz to Fz. A trend for improved reaction time at the second assessment was observed for the music trained group only. These results support the hypothesis that music training changes the neural indices of attention as assessed by the ANT in children with an FASD. This study should be extended to evaluate the effects of music training relative to a more closely matched active control and determine whether additional improvements emerge with longer term music training. Full article