The traditional multivariate signal decomposition algorithm presumes that the multichannel signal has co-frequency components. However, in real signals, the frequencies of these components are not precisely aligned and exhibit some differences. This article introduces a novel multichannel signal processing algorithm, termed grouped successive multivariate variational mode decomposition (GSMVMD). GSMVMD adopts a unique grouped expansion strategy to produce more effective co-frequencies, while we have also improved the algorithm framework by combining successive extraction strategy. GSMVMD first uses channel-by-channel variational mode decomposition (VMD) to determine the center frequency of each channel, and then performs frequency-grouped normalization. Subsequently, a variational optimization model is established based on the grouped results, and the optimal solution for GSMVMD model is obtained using the alternate direction method of multipliers (ADMMs). Finally, we conduct experiments using multichannel electroencephalogram (EEG) signals as an example to verify the superiority of the GSMVMD algorithm, and design a grouped standard deviation (GSD) of degree parameters to measure the connectivity between EEG channels, providing a new analytical perspective.

Eye-tracking research offers valuable insights into human gaze behavior by examining the neurophysiological mechanisms that govern eye movements and their dynamic interactions with external stimuli. This review explores the foundational principles of oculomotor control, emphasizing the neural subsystems responsible for gaze stabilization and orientation. Although controlled laboratory studies have significantly advanced our understanding of these mechanisms, their ecological validity remains a critical limitation. However, the emergence of mobile eye tracking technologies has enabled research in naturalistic environments, uncovering the intricate interplay between gaze behavior and inputs from the head, trunk, and sensory systems. Furthermore, rapid technological advancements have broadened the application of eye-tracking across neuroscience, psychology, and related disciplines, resulting in methodological fragmentation that complicates the integration of findings across fields. In response to these challenges, this review underscores the distinctions between head-restrained and naturalistic conditions, emphasizing the importance of bridging neurophysiological insights with experimental paradigms. By addressing these complexities, this work seeks to elucidate the diverse methodologies employed for recording eye movements, providing critical guidance to mitigate potential pitfalls in the selection and design of experimental paradigms.

This article introduces a novel extension of the multivariate variational mode decomposition (MVMD) method, termed fuzzy MVMD (FMVMD), designed to enhance alignment information extraction. In contrast to MVMD, FMVMD focuses on capturing finer alignment details by leveraging fuzzy clustering techniques. The proposed FMVMD algorithm proceeds through the following steps: First, FMVMD employs a modified clustering algorithm, termed fuzzy C-means (FCM), to categorize submodes within each channel into fuzzy clusters based on their contribution to common center frequencies. Second, a variational optimization model is formulated, extending the principles of MVMD to accommodate the fuzzy clustering approach used in FMVMD. Finally, an optimization technique called the alternating direction method of multipliers is employed to derive the optimal solution for the FMVMD model. Experimental results show that FMVMD achieves a 41% and 28% improvement in center frequency alignment performance compared to MVMD when using two and three fuzzy clusters, respectively, and a 13% improvement compared to GMVMD with the same number of clusters. Under a 25 dB SNR condition, FMVMD demonstrates a noise resistance improvement of 44% and 24% compared to MVMD with two and three fuzzy clusters, respectively, and a 37% improvement compared to GMVMD. Validation using EEG data in the forms of bipolar leads and common average reference confirms the effectiveness of FMVMD, achieving consistently favorable results.

This review analyzes 94 articles in an attempt to define the concept of presence in virtual reality (VR). Two types of data were examined: physiological variables and questionnaires, which were used in 85% study of the selected articles. The physiological measurements focused mainly on head movements, as well as electromyographic and electrocardiographic activity. Over time, a gradual decrease in the use of questionnaires is noted, with a growing preference for physiological markers to define presence in VR. We analyzed papers with physiological measurement methods and noted additional usage of subjective questionnaires. This approach captures the complexity of the subject’s experience, which includes cognitive, emotional, and physical responses. Additionally, the increasing use of artificial intelligence, particularly deep learning, is a promising trend for defining this concept. Finally, this review raises two important issues that require further investigation. Firstly, the very nature of the neurophysiological variables recorded to detect presence: they are also recommended for quantifying stress and mental load, to name but a few behavioral characteristics. Consequently, none of them can be considered specific to presence in VR. Second, the number of people tested is often small, which often poses a problem, given the wide variety of methodologies used and the physiological and psychological reactions of the people tested in VR in the 94 studies we analyzed. Clearly, there is a need for larger-scale prospective studies to better define the concept of presence during a virtual reality immersion experience.

Automatic seizure detection algorithms have significant implications for improving patients' quality of life. However, the absence of multiscale analysis in these algorithms regarding electroencephalography (EEG) signals may lead to overlooking crucial seizure features. To address this limitation, our study aims to develop a novel seizure detection algorithm based on hybrid morphological filtering optimization multiscale amplitude integrated EEG (aEEG). First, multichannel EEG signals, collected by EEG sensors, are preprocessed with asymmetric filtering, rectification, envelope detection, multiscale time compression and signal smoothing to obtain multiscale aEEG. Second, morphological filter (MF), multiscale MF (MSMF), and average combination difference MF (ACDMF) are hybridized to filter, analyze, and reconstruct the signals, thereby emphasizing the seizures. Thereafter, optimal aEEG signal is selected using the channel and time compression scale (TCS) joint optimization algorithm based on the kappa (Ka), and this signal is utilized for seizure detection. Finally, it is evaluated on the CHB-MIT database using multiple validation methods, achieving satisfactory experimental results. The results indicate that the model attains accuracy (ACC), specificity (SPE), sensitivity (SEN), F1 score ( F1 ), and Ka of 97.60%, 96.56%, 98.63%, 97.60%, and 95.19%, respectively.

Different seizure types exhibit varying levels of redundant brain network features. In order to select features that contribute significantly to model performance, an algorithm for seizure type classification based on multi-dimensional brain network feature selection (MBNFS) is proposed. Firstly, wavelet packet transform (WPT) is applied to all channels of the electroencephalogram (EEG) signals and all the sub-band signals obtained are used as network nodes. Secondly, mutual information (MI) between different nodes is calculated to get MI matrix as edge weights. Next, we design a new brain network set construction method based on the leave-one-out method and MI matrix to formulate a set of brain networks. After that, network features are extracted through nodes and edges and further evaluated for their contributions using random forest (RF). Then, based on the leave-one-out method and a matrix where the main diagonal is 0 and the remaining elements are 1, the contributions of channels, sub-bands and network features are deduced. Leveraging these three-dimensional contributions, a multi-dimensional iterative selection is conducted, focusing on selecting the dimensional individuals whose contributions exceed the manually set threshold. Finally, the RF model is trained with the refined feature set to classify seizure types, yielding test set detection accuracy, specificity, sensitivity, F1 score and kappa values of 99.86%, 99.86%, 99.86%, 0.9986 and 0.9984, respectively.

Humans' remarkable technical reasoning skills have led to the development of increasingly sophisticated tools. In particular, society has welcomed the advent and democratization of machines that produce effects through indirect causal chains. Intuitively, perfect mastery of such systems should require a detailed understanding of their underlying processes. This raises the question of the boundaries of human cognitive abilities in the context of tool use. In other words, can the human brain integrate the characteristics of any tool, or are there inherent limitations? The present study investigates the potential limits of human tool-use when faced with a complex human-machine interface. To this end, professional helicopter pilots conducted realistic flights in a high-fidelity helicopter simulator. A comprehensive analysis was then conducted on the flight trajectories, the directions of movement of their primary flight command, and the tilt of their head as a function of the aircraft's tilt in the roll plane. Our findings suggest that helicopter pilots severely restrict the capabilities of their aircraft. This simplification mechanism confines the operational range of the helicopter to conditions that elicit sensory inputs comparable to those experienced in everyday life. Our results further indicate some level of prediction regarding the sensory consequences of the motor-to-mechanical transformations. In sum, we postulate that control over complex human-machine interfaces is achieved by simplification through anthropomorphism in order to facilitate sensorimotor integration. These considerations have implications for the design of human-machine interfaces and raise safety concerns when interacting with highly sophisticated systems.

Open access, clean, annotated databases are key for future significant advances in gait quantification with inertial sensors. This multi-pathology and clinically annotated dataset provides 1356 gait trials from 260 participants equipped with four inertial measurement units placed on the head, lower back, and dorsal part of each foot. Participants followed a standardized protocol: standing still, walking 10 meters, turning around, walking back 10 meters, and stopping. It results in a large human walking dataset with over 11 hours of gait time series data. The quality is ensured by the documentation and metadata provided. The study population encompasses healthy individuals and patients with neurological (parkinson disease, cerebrovascular accident, radiation-induced leukoencephalopathy and chemotherapy-induced peripheral neuropathy) or orthopedic (hip osteoarthritis, knee osteoarthritis and anterior cruciate ligament injury) conditions. For each pathology, the most relevant clinical or radioclinical score has been calculated to provide insight into the gravity of the disease. This dataset can be used to study kinematic parameters, gait cycles time series, and various indicators for quantifying gait in routine clinical practice.

Wearable wristband device-based epilepsy detection has the merits of noninvasiveness, portability, low costs, and good environmental adaptability. However, attention has been paid to exploring the attitude angle signals collected by wearable devices for epilepsy detection. In this article, a systematic analysis of whether the wearable device-based attitude angle signals, particularly the PITCH and ROLL angles, can be applied to epilepsy seizure detection, is studied. The relationship among attitude angle signals, acceleration, and angular velocity signals at the feature level is analyzed, and the detection effectiveness of combining different attitude angle features for classifier training and testing is presented and discussed. The long-term recorded data were collected by wearable devices from 28 epileptic patients, of which 11 were from the Fourth Affiliated Hospital of Anhui Medical University and 17 from the Department of Neurology, Children’s Hospital, Zhejiang University School of Medicine. Each recording includes the measurement of three-axis acceleration (ACC), three-axis gyroscope (GYR), ROLL, PITCH, surface electromyography (SEMG), and electrodermal activity (EDA), with at least one seizure recorded for each subject. Experimental results show that ROLL and PITCH angles can be utilized for epilepsy detection, with better performance than using ACC and GYR. Moreover, the attitude angle feature training by a long short-term memory (LSTM) network can achieve the highest accuracy and efficiency.

The use of complex human-machine interfaces (HMIs) has grown rapidly over the last few decades in both industrial and personal contexts. Now more than ever, the study of mental workload (MWL) in HMI operators appears essential: when mental demand exceeds task load, cognitive overload arises, increasing the risk of work-related fatigue or accidents. In this paper, we propose a data-driven approach for the continuous estimation of the MWL of professional helicopter pilots in realistic simulated flights. Physiological and operational parameters were used to train a novel machine-learning model of MWL. Our algorithm achieves good performance (ROC AUC score 0.836 ± 0.081, the maximum F1 score 0.842 ± 0.078 and PR AUC score 0.820 ± 0.097) and shows that the operational information outperforms the physiological signals in terms of predictive power for MWL. Our results pave the way towards intelligent systems able to monitor the MWL of HMI operators in real time and question the relevancy of physiology-derived metrics for this task.

Research on the electroencephalogram-electromyogram (EEG-EMG) functional network is of great significance for exploring the correlation and diagnosing neurological diseases between EEG and EMG. To detect interictal and ictal periods of West syndrome with reliability and accuracy, this article proposes seizure detection method based on a node-optimized EEG-EMG fusion network. In contrast to conventional brain networks, the EEG-EMG fusion network constructs connections between EEG and EMG functional networks, effectively using complementary data from multiple modalities, and combines enhanced artificial rabbit optimization (ARO) algorithm to choose the ideal network node combination. First, the EEG and EMG data are preprocessed. Then, three functional networks are constructed using the preprocessed EEG signals, EMG signals, and EEG-EMG fusion signals. The EEG signals are divided into five frequency bands, and mutual information (MI) is used as an indicator for the connection between network nodes. Finally, the nodes for fusion network are chosen using enhanced ARO algorithm. The EEG and EMG data of 17 West syndrome patients are records by the Children's Hospital of Zhejiang University School of Medicine and which are evaluated by fivefold cross-validation method. The results of experiment indicate that average accuracy, precision, sensitivity, specificity, and F1 scores can reach 97.72%, 97.73%, 97.86%, 97.38%, and 97.54%, respectively.

Onasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT. Clinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors. 23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001). IMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

Encephalitis is a serious disease for neurological dysfunction caused by inflammation of the brain parenchyma. Recurrent convulsive seizures or nonconvulsive status are the main causes of many neurological sequelae. Accurately identifying convulsive seizure signals in continuous electroencephalogram (EEG) signals collected from patients with severe encephalitis can help doctors effectively make diagnoses and give treatment plans. Traditionally, fourfold-scale compressed amplitude-integrated electroencephalography (aEEG) is used for detection. In this article, a novel convulsive seizure detection method of encephalitis based on multiscale aEEG signal is proposed. First, continuous EEG (cEEG) signal is converted into multiscale aEEG. Second, multiscale shapelets from multiscale aEEG signals are extracted to form a multiscale ictal waveform codebook. Third, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the codebook and the actual waveform. Finally, random forest (RF) classifier is applied to train and test the method. In the dataset collected, the accuracy, sensitivity based on the event, specificity, F1 score, mean absolute error, missed detection, false detection, and false positive ratio based on the event obtained by the proposed method reach 96.32%, 95.70%, 97.22%, 96.85%, 0.037, 4.71%, 3.70%, and 0.06 times/h, respectively.

BackgroundPrecise monitoring of the Depth of Anesthesia (DoA) is essential to prevent intra-operative awareness (in case of underdosage) or increased post-operative morbi-mortality (in case of overdosage). The recording of a high- frequency multimodal monitoring during general anesthesia (GA) and the capability of classification of dynamic networks should have the potential to help predicting the DoA in a clinical practice. In this study, we aimed at predicting the DoA according four levels (Awake, Loss of Consciousness (LOC), Anesthesia, Return of Consciousness (ROC), Emergence) thanks to a Hidden Markov Model (HMM) relying on four common physiologic variables: Mean Blood Pressure (MBP), Heart Rate (HR), Respiratory Rate (RR), and end-expiratory concentration of sevoflurane (AAEt).MethodsAfter induction by sufentanil and propofol, the anesthesia was maintained by sevoflurane. We recorded the physiological variables at a high frequency during all the procedure [cardiopulmonary variables, AAEt, 2- channel ElectroEncephaloGraphy (EEG) data, and BIS values]. In the training phase, the different states (Awake, LOC, Anesthesia, ROC, Emergence) were identified according to the reading of the spectrograms of the two EEG channels. However, the prediction with the HMM were only based on the four physiological variables.ResultsOn a dataset consisting of 60 patients under general anaesthesia, results suggested that the HMM had a true positive rate (TPR) for identifying Awake, Anesthesia and Emergence of 88%, 72% and 58%, respectively.ConclusionTo our knowledge, this is the first application of such a model to identify the DoA without relying on EEG data. We suggest that a HMM can help the anesthetist monitoring the DoA out of a set of current physiologic variables without necessity of brain monitoring. The model could be improved by increasing the number of patients in the database and accuracy would probably benefit from adding in the model the data of a single EEG channel.

This article thoroughly describes a data set of 240 multivariate time series collected using 34 Cartesian Optoelectronic Dynamic Anthropometer (CODA) markers placed on the upper limb of 16 healthy subjects each undergoing 15 predefined movements such as raising their arms or combing their hair. Each sensor records its position in the 3D space. In total, 2.5 hours of time series are collected. A remarkable aspect of this data set is the extensive availability of metadata: subjects' characteristics (age, height, etc.) as well as movements' annotations. Indeed, for each subject and each movement, the start and end time stamps of at least two iterations of the same movement are provided. In addition to the study of human motion, this data set can be used to evaluate generic time series analytical tasks such as multivariate time series segmentation, clustering or classification.

West syndrome is a persistent disease with a common, age-dependent, severe neurological disorder that poses a substantial risk to intellectual and motor deficits in children. Research on intelligent seizure detection methods for West syndrome can help doctors make effective judgments. In this article, we propose an infantile spasm seizure detection algorithm based on hybrid optimization of entropy of entropy (EoE) and Shannon entropy (SE). Different from traditional EoE and SE, fusion features are extracted from the optimal division of EoE (ODEoE) and optimal SE (OSE) by considering the epoch length and the number of subepochs in an epoch jointly. In addition, multivariate entropy parameterization via enhanced multistrategy sparrow algorithm (MEPEM-SSA) is employed for choosing the ideal parameters. Finally, the data from Zhejiang University School of Medicine Children's Hospital (ZUCH) dataset, the CHB-MIT dataset, and the Siena scalp electroencephalogram (EEG) dataset are extracted using EEG sensors, and a tenfold cross-validation evaluation is performed on these EEG data from West syndrome patients. The experimental results on the ZUCH dataset show that the mean values of precision, specificity, sensitivity, and ${F}1$ score of the algorithm are 98.08%, 97.93%, 98.46%, and 98.09% respectively. On the CHB-MIT dataset, the mean values of precision, specificity, sensitivity, and ${F}1$ score reach 98.76%, 99.27%, 97.94%, and 98.59%, respectively. On the Siena Scalp EEG dataset, the mean values of precision, specificity, sensitivity, and ${F}1$ score reach 98.33%, 98.04%, 98.39%, and 98.20%, respectively.

In a recent review, we summarized the characteristics of perceptual-motor style in humans. Style can vary from individual to individual, task to task and pathology to pathology, as sensorimotor transformations demonstrate considerable adaptability and plasticity. Although the behavioral evidence for individual styles is substantial, much remains to be done to understand the neural and mechanical substrates of inter-individual differences in sensorimotor performance. In this study, we aimed to investigate the modulation of perceptual-motor style during locomotion at height in 16 persons with no history of fear of heights or acrophobia. We used an inexpensive virtual reality (VR) video game. In this VR game, Richie’s Plank, the person progresses on a narrow plank placed between two buildings at the height of the 30th floor. Our first finding was that the static markers (head, trunk and limb configurations relative to the gravitational vertical) and some dynamic markers (jerk, root mean square, sample entropy and two-thirds power law at head, trunk and limb level) we had previously identified to define perceptual motor style during locomotion could account for fear modulation during VR play. Our second surprising result was the heterogeneity of this modulation in the 16 young, healthy individuals exposed to moving at a height. Finally, 56% of participants showed a persistent change in at least one variable of their skeletal configuration and 61% in one variable of their dynamic control during ground locomotion after exposure to height.

Seizure detection is traditionally done using video/electroencephalography monitoring, but for out-of-hospital patients, this method is costly. In recent years, portable device to detect seizures gains attention. In this paper, multimodal signals collected by portable devices are studied, and a seizure detection algorithm is proposed based on adaptive multi-bit local differential ternary pattern (MLDTP). This algorithm is used for detecting seizure period and inter-seizure period. Traditional local binary pattern has certain limitations in describing one-dimensional time series signals. It can only describe two types of structures in signals: Rising structure and falling structure, making the signal patterns overly monotonous and not conducive to classification tasks. To address this issue, this paper introduces two additional structures, slowly rising structure and slowly falling structure, into the signal description using MLDTP method. This method constructs multi-bit neighboring relationships of the signals, and adaptively selects the optimal MLDTP parameters for different modalities using the Archimedes optimization algorithm (AOA). Additionally, this paper extensively discusses a multimodal signal fusion strategy, mapping features of different modal signals to the same feature space through the MLDTP algorithm to achieve information complementarity. Long-term recorded data from 18 patients were collected using the wearable device Biovital P1, with 13 cases from the Children’s Hospital affiliated with Children’s Hospital, Zhejiang University School of Medicine, and 5 cases from the fourth Affiliated Hospital of Anhui Medical University. The dataset underwent five-fold cross-validation, resulting in average accuracy, precision, sensitivity and F1 score of 96.81%, 98.55%, 95.24% and 96.87%, respectively.

The prevalence of physical inactivity after stroke is high and exercise training improves many outcomes. However, access to community training protocols is limited, especially in low-income settings.To investigate the feasibility and efficacy of a new intervention: Circuit walking, balance, cycling and strength training (CBCS) on activity of daily living (ADL) limitations, motor performance, and social participation restrictions in people after stroke.Forty-six community-dwelling individuals with chronic stroke who were no longer in conventional rehabilitation were randomized into an immediate CBCS group (IG; initially received CBCS training for 12 weeks in phase 1), and a delayed CBCS group (DG) that first participated in sociocultural activities for 12 weeks. In phase 2, participants crossed over so that the DG underwent CBCS and the IG performed sociocultural activities. The primary outcome was ADL limitations measured with the ACTIVLIM-Stroke scale. Secondary outcomes included motor performance (balance: Berg Balance Scale [BBS], global impairment: Stroke Impairment Assessment Set [SIAS] and mobility: 6-minute and 10-metre walk tests [6MWT and 10mWT] and psychosocial health [depression and participation]). Additional outcomes included feasibility (retention, adherence) and safety.ADL capacity significantly improved pre to post CBCS training (ACTIVLIM-stroke, +3,4 logits, p < 0.001; effect size [ES] 0.87), balance (BBS, +21 points, p < 0.001; ES 0.9), impairments (SIAS, +11 points, p < 0.001; ES 0.9), and mobility (+145 m for 6MWT and +0.37 m/s for 10mWT; p < 0.001; ES 0.7 and 0.5 respectively). Similar improvements in psychosocial health occurred in both groups. Adherence and retention rates were 95% and 100%, respectively.CBCS was feasible, safe and improved functional independence and motor abilities in individuals in the chronic stage of stroke. Participation in CBCS improved depression and social participation similarly to participation in sociocultural activities. The benefits persisted for at least 3 months after intervention completion.PACTR202001714888482