IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Low-Intensity Focused Ultrasound Stimulation on Fingertip Can Evoke Fine Tactile Sensations and Different Local Hemodynamic Responses","authors":"Liuni Qin;Mingyang Dou;Lili Niu;Laixin Huang;Fei Li;Shichun Bao;Xinping Deng;Guanglin Li;Yanjuan Geng","doi":"10.1109/TNSRE.2024.3493925","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3493925","url":null,"abstract":"Low-intensity focused ultrasound stimulation (LIFUS) has been proved effective in eliciting vibrotactile in addition to warm, cold and nociceptive pain when applied to human peripheral endings. However, if it can evoke fine tactile sensations has been rarely investigated by far despite the importance of fine tactile feedback in motor control. To explore this issue, 14 healthy volunteers were recruited in this study. A psychophysical experiment was firstly conducted to determine the appropriate range of pulse repetition frequency (PRF) and acoustic intensity (AI). Then, participants were asked to perceive and discriminate different tactile stimulations under LIFUS, so as to evaluate if multiple fine tactile sensations could be reliably elicited by modulating the PRF and AI. For objective assessment, the local blood perfusion volume (BPV) response beneath stimulated fingertip was recorded and characterized. Our results showed that four types of tactile sensations, including tapping, vibrating, electrical, and pressure could be reliably elicited by modulating the PRF and AI within a specific range, and there was a significant impact of PRF and AI on both participants’ tactile discrimination and amplitude features of BPV response. This study would facilitate the application of LIFUS to some human-machine interaction scenarios, and shed valuable insights on the physiological mechanisms of peripherally applied ultrasound stimulation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4086-4097"},"PeriodicalIF":4.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface","authors":"Kirill Kokorin;Syeda R. Zehra;Jing Mu;Peter Yoo;David B. Grayden;Sam E. John","doi":"10.1109/TNSRE.2024.3500217","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3500217","url":null,"abstract":"Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully-autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected \u0000<inline-formula> <tex-math>$alpha lt 0.05$ </tex-math></inline-formula>\u0000) mean task success rate (\u0000<inline-formula> <tex-math>${p} lt 0.0001$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$mu =36.1$ </tex-math></inline-formula>\u0000%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (\u0000<inline-formula> <tex-math>${p} lt 0.0001$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$mu = -26.8$ </tex-math></inline-formula>\u0000%, 95% CI [−36.0%, −17.7%]), and participant workload (\u0000<inline-formula> <tex-math>${p} =0.02$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$mu = -11.6$ </tex-math></inline-formula>\u0000, 95% CI [−21.1, −2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4098-4108"},"PeriodicalIF":4.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Neural Basis of the Effect of Transcutaneous Auricular Vagus Nerve Stimulation on Emotion Regulation Related Brain Regions: An rs-fMRI Study","authors":"Yujiao Zhang;Pan Lin;Ruimin Wang;Jiang Zhou;Xiaoquan Xu;Jianwei Wang;Sheng Ge","doi":"10.1109/TNSRE.2024.3497893","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3497893","url":null,"abstract":"Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising neurostimulation approach for emotion regulation. This research aimed to clarify the underlying neural basis responsible for taVNS’s impact on emotional regulation related brain regions. Thirty-two healthy volunteers were allocated into a taVNS group, which received electrical stimulation at the concha area of the ear, and a sham group, which received earlobe stimulation. Resting-state functional magnetic resonance imaging data were collected from both the taVNS and sham groups pre- and post-stimulation. To evaluate the alterations in neural activity and connectivity resulting from auricular electrical stimulation, degree centrality and functional connectivity analyses were used. The results indicated that taVNS modulated the neural activity of several brain regions, including the bilateral precuneus, temporal gyrus, precentral gyrus, and postcentral gyrus, whereas earlobe stimulation did not produce such effects. taVNS may improve emotion regulation by modulating neural activation and functional connectivity in key brain regions, then facilitating the integration of emotional responses, memories, and experiences. Thus, these brain regions may serve as potential therapeutic targets for taVNS in treating disorders associated with emotional dysregulation. These findings provide insight into the neural basis through which taVNS influences emotion regulation and hold potential for the development of neuromodulation-based therapeutic strategies for emotional disorders.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4076-4085"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Asynchronous Training-free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios.","authors":"Junsong Wang, Yuntian Cui, Hongxin Zhang, Haolin Wu, Chen Yang","doi":"10.1109/TNSRE.2024.3496727","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3496727","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dyslexia Analysis and Diagnosis Based on Eye Movement.","authors":"R Vaitheeshwari, Chen Chih-Hsuan, Chia-Ru Chung, Hsuan-Yu Yang, Shih-Ching Yeh, Eric Hsiao-Kuang Wu, Mukul Kumar","doi":"10.1109/TNSRE.2024.3496087","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3496087","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding a Cognitive Performance State from Behavioral Data in the Presence of Auditory Stimuli.","authors":"Saman Khazaei, Rose T Faghih","doi":"10.1109/TNSRE.2024.3495704","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3495704","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>We decode the performance from behavioral observation data using the Bayesian state-space approach. Forming an observation from the paired binary response with the associated continuous reaction time may lead to an overestimation of the performance, especially when an incorrect response is accompanied by a fast reaction time. We apply the marked point process (MPP) framework such that the performance decoder takes the correct/incorrect responses and the reaction time associated with correct responses as an observation. We compare the MPP-based performance with two other decoders in which the pairs of binary and continuous signals are taken as the observation; one decoder considers an autoregressive (AR) model for the performance state, and the other one employs an autoregressive-autoregressive conditional heteroskedasticity (AR-ARCH) model which incorporates the time-varying and adaptive innovation term within the model.</p><p><strong>Results: </strong>To evaluate decoders, we use the simulated data and the n-back experimental data in the presence of multiple music sessions.</p><p><strong>Conclusion: </strong>The Bayesian state-space approach is a promising way to decode the performance state. With respect to individual perspective, the estimated MPP-based and ARCH-based performance states outperform the AR-based estimation. Based on the aggregated data analysis, the ARCH-based performance decoder outperforms the other decoders.</p><p><strong>Significance: </strong>Performance decoders can be employed in educational settings and smart workplaces to monitor one's performance and contribute to developing a feedback controller in closed-loop architecture to improve cognitive performance.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Gluteus-Specific Muscle Synergy Recruited During the First Recovery Step Following a Backward Pitch Perturbation","authors":"Huijie Lin;Xiping Ren;Christoph Lutter;Haidan Liang;Fengxue Qi;Qining Yang;Maeruan Kebbach;Martin Schlegel;Sven Bruhn;Rainer Bader;Thomas Tischer","doi":"10.1109/TNSRE.2024.3495514","DOIUrl":"10.1109/TNSRE.2024.3495514","url":null,"abstract":"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.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4033-4041"},"PeriodicalIF":4.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10749836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Swing-Assist Controller for Enhancing Knee Flexion in a Semi-Powered Transfemoral Prosthesis","authors":"David M. Marsh;Marco Puliti;Michael Goldfarb","doi":"10.1109/TNSRE.2024.3495517","DOIUrl":"10.1109/TNSRE.2024.3495517","url":null,"abstract":"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.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4052-4062"},"PeriodicalIF":4.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10749997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergy-Based Estimation of Balance Condition During Walking Tests","authors":"Kaitai Li;Heyuan Wang;Xuesong Ye;Congcong Zhou","doi":"10.1109/TNSRE.2024.3495530","DOIUrl":"10.1109/TNSRE.2024.3495530","url":null,"abstract":"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 (\u0000<inline-formula> <tex-math>$mathbf {R}^{mathbf {{2}}}= ,, 0.87~pm ~0.07$ </tex-math></inline-formula>\u0000) and GRF (\u0000<inline-formula> <tex-math>$mathbf {R}^{mathbf {{2}}}= ,, 0.83~pm ~0.09$ </tex-math></inline-formula>\u0000) 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.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"32 ","pages":"4063-4075"},"PeriodicalIF":4.8,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10749971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in Spatiotemporal Variability of Muscular Response under the Influence of Post-stroke Spasticity.","authors":"Tian Xie, Yan Leng, Ruxin He, Chuhuai Wang, Rong Song","doi":"10.1109/TNSRE.2024.3486026","DOIUrl":"https://doi.org/10.1109/TNSRE.2024.3486026","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}