Low-intensity pulsed ultrasound stimulation (LIPUS) as a non-invasive, high-spatial resolution and high penetration depth brain modulation technology has been used for modulating neuromuscular function. However, the modulation of neural electrical signal changes in the neuromuscular system by LIPUS remains to be explored. In this study, we stimulated the mouse brain motor cortex by LIPUS with different number of tone burst (NTB) and recorded the local field potential (LFP) signals of the target region and electromyography (EMG) of tail muscle. Multi-Scale Transfer Entropy (MSTE) analysis method was used to explore the multi-scale synchronization characteristics and functional cortico-muscular coupling (FCMC) strength changes of mice LFP-EMG before and after LIPUS under different NTBs. The results show that the MSTE of LFP-EMG before and after LIPUS stimulation was higher than that of EMG-LFP. After adding multi-scale, MSTE has a significant relationship with time scales. When NTB = 200, the scale of extremum is the largest. There was a fitting intersection between LFP-EMG and EMG-LFP scale 7-21 before and after stimulation. After scale averaging, the LFP-EMG after stimulation was lower than that before stimulation, and the EMG-LFP after stimulation was higher than that before stimulation.Conclusion: There is a significant correlation between NTB and time scale before and after LIPUS,as well as upward and downward. Consequently,This study used FCMC methods to study different NTBs and multi-scale relationships, provides new variables from LIPUS parameters and analysis, and provides new reference for clinical applications of LIPUS.

Stroke remains a leading cause of adult disability, underscoring why research continues to focus on advancing new treatment methods and neurophysiological indexes. While these studies may be effective, many lack a clear theoretical framework. The current study first determined the optimal combination effects of mirror therapy (MT) with transcranial direct current stimulation (tDCS) on the premotor or primary motor cortex on its short-term and sustained clinical outcomes. We then introduced electroencephalogram (EEG) indexes derived from the gating-by-inhibition model to explore the underlying therapeutic mechanisms. The EEG indexes used in this study focused on the functional involvement for motor generation: alpha power at temporal regions (inhibiting non-motor activity) and central-frontal regions (releasing motor regions from inhibition). Results showed that post-training benefits, measured by Fugl-Meyer Assessment (FMA), were similar across 3 tDCS interventions (premotor, primary motor, sham). EEG seemed more sensitive to the training, with notable responses in the premotor tDCS group. Three months after training, only the premotor tDCS group maintained the gains in FMA, with these improvements correlated with the EEG indexes. Again, this pattern was specific to premotor tDCS. Since the gating-by-inhibition model suggests that EEG index reflects an individual's psychomotor efficiency, we also found that the baseline EEG index could predict FMA retention. Our findings demonstrate the superiority of combined premotor tDCS with MT and identify functionally oscillatory alpha-band activity in the temporal and central-frontal regions as potentially underlying the therapeutic mechanism. An individual's spatial pattern of EEG may be effective in predicting upper extremity retention effect.

Deep brain stimulation (DBS) is an effective treatment for neurological disorders, and accurately reconstructing the DBS lead trajectories is crucial for MRI compatibility assessment and surgical planning. This paper presents a novel fully automated framework for reconstructing DBS lead trajectories from postoperative CT images. The leads were first segmented by thresholding, but would be fused together somewhere. Mean curvature analysis of multi-layer CT number isosurfaces was introduced to effectively address lead fusion, due to the different topological characteristics of the isosurfaces in and out of the fusion regions. The position of electrode contacts was determined through morphological analysis to get the starting point and the initial direction for trajectory tracking. The next trajectory point was derived by calculating the weighted average coordinates of the candidate points, using the distance from the current estimated trajectory and the CT number as weights. This method has demonstrated high accuracy and efficiency, successfully and automatically reconstructing complex bilateral trajectories for 13 patient cases in less than 10 minutes with errors less than 1 mm. This work overcomes the limitations of existing semi-automatic techniques that require extensive manual intervention. It paves the way for optimizing DBS lead trajectory to reduce tissue heating and image artifacts, which will contribute to neuroimaging studies and improve clinical outcomes. Code for our proposed algorithm is publicly available on Github.

Dyslexia is a complex reading disorder characterized by difficulties in accurate or fluent word recognition, poor spelling, and decoding abilities. These challenges are not due to intellectual, visual, or auditory deficits. The diagnosis of dyslexia is further complicated by symptom variability, influenced by cultural and personal factors. This study leverages Virtual Reality (VR) advancements, eye movement tracking, and machine learning to create a virtual reading environment that captures eye movement data. This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. Our findings suggest that this model significantly enhances the accuracy and efficiency of dyslexia diagnosis, marking an important advancement in educational technology and providing robust support for individuals with dyslexia. Although the sample size was limited to 10 dyslexic and 4 control participants, the results offer valuable insights and lay the groundwork for future studies with larger cohorts.

Spasticity is characterized by involuntary muscle activation due to the hyperexcitability of the stretch reflex. However, the temporal pattern and spatial heterogeneity of motor unit recruitment in this activation are not fully understood. The aim of this study was to investigate changes in spatiotemporal variability in spastic muscle responses to passive stretch after stroke. We acquired high-density surface electromyography (HD-sEMG) signals from the biceps brachii in 21 stroke survivors, 10 age-matched old healthy controls, and 10 young healthy controls at velocities of 10°/s, 60°/s, 120°/s, and 180°/s. The average and distribution of fuzzy entropy extracted from HD-sEMG signals were used to characterize the spatiotemporal variability of muscle activation. Our results showed that the temporal variability of stroke survivors was significantly lower than that of old controls, while the latter was significantly lower than that of young controls. As velocity increased, only stroke survivors showed decreased activation variability and increased activation levels in specific region, and the distributions were similar across velocities. Notably, the activation variability and the size of regions with high activation levels were significantly negatively and positively correlated with the severity of spasticity, respectively. These findings provide entropy-based evidence for the contribution of greater silencing of motoneurons responsible for reflex inhibition to the synchronous recruitment of more motor units.

This work proposes a new swing controller for semi-powered low impedance transfemoral prostheses that resolves the issue of potentially competing inputs between artificial assistive power and user-sourced power. Rather than add power as an exogeneous input, the control approach uses power to modify the homogeneous portion of the shank dynamics, and therefore need not construct or curate an input that is coordinated with user input. The implemented controller requires a single control parameter at a given walking speed, where the value of that parameter is a function of walking speed, as determined by an adaptive algorithm, such that peak knee angles are commensurate with walking-speed-dependent behaviors of individuals without any negative gait pathologies. The controller and parameter selection algorithm are described in the paper, and subsequently validated in walking experiments with three participants with unilateral transfemoral amputation. The experiments demonstrate that the proposed controller increases peak knee angle and minimum toe clearance during swing phase without increasing hip compensatory actions, relative to the users' daily-use devices.

In the area of human-machine interface research, the continuous estimation of the Center of Pressure (COP) in the human body can assess users' balance conditions, thereby effectively enhancing the safety and diversity of studies. This paper aims to present a novel method for continuous synergy-based estimation of human balance states during walking, and simultaneously analyze the impact of various factors on the estimation results. Specifically, we introduce muscle synergy coherence features and analyze the variations of these features in different balance conditions. Furthermore, we fuse temporal features extracted by a bidirectional long short-term memory (BILSTM) network with spatial features derived from the analysis of muscle synergy coherence to continuously estimate the mediolateral COP and Ground Reaction Force (GRF) during human walking tests. Then, we analyze the influence of different electromechanical delay compensation (EMD) time, the number of synergies, and different walking speeds on the estimation results. Finally, we validate the estimation capability of the proposed method on data collected in real-world walking tests. The results indicate a significant correlation between the proposed muscle synergy coherence features and balance conditions. The network structure combining muscle synergy coherence features and BILSTM features enables accurate continuous estimation of COP (R=0.87±0.07) and GRF (R=0.83±0.09) during walking tests. Our research introduces a novel approach to the continuous estimation of balance conditions in human walking, with potential implications in various applications within human-machine engineering, such as exoskeletons and prosthetics.

Cognitive performance state is an unobserved state that refers to the overall performance of cognitive functions. Deriving an informative observation vector as well as the adaptive model and decoder would be essential in decoding the hidden performance.

The central nervous system momentarily activates a set of specific muscle synergies to maintain balance when external mechanical perturbations induce walking instability, which is critically involved in preventing falls. The activation patterns and composition of the muscle synergies recruited in the perturbed leg have not been fully characterized, and even less so for the recovery step. Here, we addressed this research gap by measuring the surface electromyographic data of the relevant muscles during a backward-pitched perturbed walk, and then extracting muscle synergy-related parameters using a non-negative matrix factorization algorithm. Our findings indicated that (1) a common set of four muscle synergies was activated in normal, perturbated and first recovery steps; (2) a specific muscle synergy controlled hip movement was recruited in the first recovery step; and (3) the main temporal activation phases of several muscle synergies were prolonged in the perturbed or the first recovery step. These results emphasize the potential significance of exploring the neurological control strategies of muscle synergy in fall prevention.

Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large character sets. The Chinese characters, including over 3500 commonly used characters with 10.3 strokes per character on average, represent a highly complex writing system. This paper proposes a Chinese handwriting BCI system, which reconstructs multi-stroke handwriting trajectories from brain signals. Through the recording of cortical neural signals from the motor cortex, we reveal distinct neural representations for stroke-writing and pen-lift phases. Leveraging this finding, we propose a stroke-aware approach to decode stroke-writing trajectories and pen-lift movements individually, which can reconstruct recognizable characters (accuracy of 86% with 400 characters). Our approach demonstrates high stability over 5 months, shedding light on generalized and adaptable handwriting BCIs.

SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional raw polysomnograms or two-dimensional spectrograms, which omit critical details due to single-view processing. This shortcoming is particularly apparent in pediatric sleep staging, where the lack of a specialized network fails to meet the needs of precision medicine. Therefore, we introduce AFSleepNet, a novel attention-based multi-view feature fusion network tailored for pediatric sleep analysis. The model utilizes multimodal data (EEG, EOG, EMG), combining one-dimensional convolutional neural networks to extract time-invariant features and bidirectional-long-short-term memory to learn the transition rules among sleep stages, as well as employing short-time Fourier transform to generate two-dimensional spectral maps. This network employs a fusion method with self-attention mechanism and innovative pre-training strategy. This strategy can maintain the feature extraction capabilities of AFSleepNet from different views, enhancing the robustness of the multi-view model while effectively preventing model overfitting, thereby achieving efficient and accurate automatic sleep stage analysis. A "leave-one-subject-out" cross-validation on CHAT and clinical datasets demonstrated the excellent performance of AFSleepNet, with mean accuracies of 87.5% and 88.1%, respectively. Superiority over existing methods improves the accuracy and reliability of pediatric sleep staging.

IN the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows: ITR=[logK+PlogP+(1-p)log(1-P/K-1)]×60/T (1).

Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A "wider range, stronger intensity, greater connection" ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.

Depressive disorder has been known to be associated with high variability in resting-state electroencephalography (EEG) signals. However, this phenomenon is often ignored in stimulus-related brain activities. This study proposed a new method to explore the EEG variability evoked by transcranial magnetic stimulation (TMS, TMS-EEG) in depressive disorder (DE) patients. The TMS-EEG data were collected from 34 DE patients and 36 healthy controls (HC). The maximum eigenvalue of the real binary correlation matrix, calculated between different trials using cross-correlation and surrogate methods, was extracted to assess trial-by-trial variability (TTV) of TMS-EEG. The new method was found to more sensitive and reliable than the standard deviation method. DE patients exhibited significantly smaller TTV in Gamma band and greater TTV in Delta band than HC. Furthermore, the HAMD-17 scores were negatively correlated with TTV values in Gamma band. This study represented the first investigation into the TTV in TMS-EEG data and revealed abnormal values in DE patients. Those findings enhance our understanding of TMS-EEG technology and provide valuable insights for studying the characteristics of DE.

Effectively integrating the time-space-frequency information of multi-modal signals from armband sensor, including surface electromyogram (sEMG) and accelerometer data, is critical for accurate gesture recognition. Existing approaches often neglect the abundant spatial relationships inherent in multi-channel sEMG signals obtained via armband sensors and face challenges in harnessing the correlations across multiple feature domains. To address this issue, we propose a novel multi-feature fusion network with spatial partitioning strategy and cross-attention (MFN-SPSCA) to improve the accuracy and robustness of gesture recognition. Specifically, a spatiotemporal graph convolution module with a spatial partitioning strategy is designed to capture potential spatial feature of multi-channel sEMG signals. Additionally, we design a cross-attention fusion module to learn and prioritize the importance and correlation of multi-feature domain. Extensive experiment demonstrate that the MFN-SPSCA method outperforms other state-of-the-art methods on self-collected dataset and the Ninapro DB5 dataset. Our work addresses the challenge of recognizing gestures from the multi-modal data collected by armband sensor, emphasizing the importance of integrating time-space-frequency information. Codes are available at https://github.com/ZJUTofBrainIntelligence/MFN-SPSCA.

This study examined how gait adaptation during predictable and non-predictable obstacle avoidance affects the sensorimotor network in both healthy controls (HC) and persons with multiple sclerosis (pwMS). We utilized fNIRS measurements of HbO2 and HHb to estimate cortical activations and connectivity networks, which were then analyzed using power spectral density (PSD) and partial directed coherence (PDC). The findings revealed distinct patterns of cortical activation and connectivity for each task condition in both groups. Healthy individuals displayed lower cortical activations in the bilateral motor cortex (MC) during non-predictable obstacle avoidance, indicating efficient neural processing. On the other hand, pwMS exhibited lower cortical activations across most brain areas during non-predictable tasks, suggesting potential limitations in neural resource allocation. When tasks were combined, pwMS demonstrated higher cortical activation across all recorded brain areas compared to HC, indicating a compensatory mechanism to maintain gait stability. Functional connectivity analysis revealed that pwMS recruited higher bilateral somatosensory association cortex (SAC) than HC, whereas healthy individuals engaged more bilateral premotor cortices (PMC). These findings suggest alterations in sensorimotor integration and motor planning in pwMS. Four machine learning models (KNN, SVM, DT, and DA) achieved high classification accuracies (92-99%) in differentiating between task conditions within each group. These results highlight the potential of integrating fNIRS-based cortical activation and connectivity measures with machine learning as biomarkers for MS-related impairments in cognitive-motor interaction. Such biomarkers could aid in predicting future mobility decline, fall risk, and disease progression.

Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient during rehabilitation or when aiding essential daily tasks such as sit-to-walk (STW). STW itself can be generalized into strategies based on individual needs and movement patterns. In this study, three long short-term memory (LSTM) neural networks were empirically trained for hip and knee torque prediction considering these STW strategies and subject anthropometry. The hip and knee are the drivers of STW, while the network architectures were selected for recognizing temporal and spatial relationships. Performance of the LSTMs were compared and evaluated against the STW strategies to accurately generate strategy-specific and user-oriented torque. As such, train and test STW data were obtained from 65 subjects across three age groups: young, middle-aged, and older adults (19-73 years). Model inputs were hip and knee angles with horizontal center of mass velocity, while windowing allowed the LSTMs to dynamically adapt to real-time changes in STW transitions. The encoder-decoder LSTM showcased optimal performance with robust recognition of temporal features. It produced significantly ( [Formula: see text]) low hip and knee root mean square error ( 0.24 ± 0.07 and 0.15 ± 0.02 Nm/kg), strong Spearman's correlation ( 93.43 ± 2.86 and 84.83 ± 2.96 %) and good intraclass correlation coefficients (greater than 0.75), demonstrating model reliability. Hence, this network predicts strategy and user oriented reference torques for personalized controllers in assistive devices, with more natural application of assistance.

This paper aims to introduce HDE-Array (High-Density Electrode Array), a novel dry electrode array for acquiring High-Density surface electromyography (HD-sEMG) for hand position estimation through RPC-Net (Recursive Prosthetic Control Network), a neural network defined in a previous study. We aim to demonstrate the hypothesis that the position estimates returned by RPC-Net using HD-sEMG signals acquired with HDE-Array are as accurate as those obtained from signals acquired with gel electrodes. We compared the results, in terms of precision of hand position estimation by RPC-Net, using signals acquired by traditional gel electrodes and by HDE-Array. As additional validation, we performed a variance analysis to confirm that the presence of only two rows of electrodes does not result in an excessive loss of information, and we characterized the electrode-skin impedance to assess the effects of the voltage divider effect and power line interference. Performance tests indicated that RPC-Net, used with HDE-Array, achieved comparable or superior results to those observed when used with the gel electrode setup. The dry electrodes demonstrated effective performance even with a simplified setup, highlighting potential cost and usability benefits. These results suggest improvements in the accessibility and user-friendliness of upper-limb rehabilitation devices and underscore the potential of HDE-Array and RPC-Net to revolutionize control for medical and non-medical applications.

We aimed to gather evidence on the feasibility of using simulator-based driving assessments for prescribing powered mobility devices (PMDs). Therefore, we compared the driving characteristics of real driving and two types of simulated driving. Thirty participants with difficulty walking more than 100 meters independently were enrolled. We developed a full-cabin and desktop simulator and created driving scenarios that closely resembled a real driving route in a park. They participated in three separate driving sessions, each using a powered wheelchair, full-cabin simulator, and desktop simulator. The driving characteristics, such as driving distance, mean speed, and standard deviation (SD) of speed, were obtained and analyzed to assess differences and correlations. Statistically significant differences were found in the driving distance and the SD of speed, respectively. However, for the mean speed, there was no statistically significant difference among the three types of driving. The intraclass correlation coefficient (ICC) for the driving distance was 0.154, which was not statistically significant. However, for mean speed, the ICC was 0.752, indicating a strong correlation. The ICC for the SD of speed was 0.562, indicating a moderate correlation. We demonstrated that the two types of simulators have characteristics that are similar to real-world driving characteristics. The mean speed showed the highest similarity, and the SD of the speed showed a moderate degree of similarity. These results highlight the significant potential of employing simulator-based driving to evaluate the use of PMDs.

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.