2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献_第6页

Safe Retrieval of a Stent-Based Endovascular Neural Recording Array 基于支架的血管内神经记录阵列的安全检索

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123895

V. S. A. Tarigoppula, G. Rind, S. Ronayne, Andrew Stent, C. D. Eiber, T. Oxley, N. Opie

{"title":"Safe Retrieval of a Stent-Based Endovascular Neural Recording Array","authors":"V. S. A. Tarigoppula, G. Rind, S. Ronayne, Andrew Stent, C. D. Eiber, T. Oxley, N. Opie","doi":"10.1109/NER52421.2023.10123895","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123895","url":null,"abstract":"The ability to retrieve a device implanted in humans has implications for the device's safety, efficacy, adoptability, and clinical and applicational flexibility. The Stentrode, a stent-based endovascular neural recording array, is a relatively new modality to capture brain data which accesses the brain in a minimally invasive manner through the body's natural highways (i.e., blood vessels). As we further characterize the safety profile for endovascular neural recording and expand on the existing applications of our endovascular neural recording array, we sought to assess the possibility of retrieving the device following a short implantation period in a Dural sinus. To demonstrate functional neural recording, steady-state visual evoked potentials were captured at 4 and 6 days post-implantation. To demonstrate safety, we analyzed histological sections of the implanted dural sinus and contralateral non-implanted sinus as a within-subject control. We show, in an ovine model, that retrieval of a stent-based endovascular neural recording array from the transverse sinus, following 7 days of implantation and neural data recording, can be performed successfully and safely with minimal effect on animals' general health and the Dural sinuses.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction of nerve functional topography using recruitment curves enables selective electrical stimulation 利用恢复曲线重建神经功能地形，实现选择性电刺激

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123775

Simone Romeni, Bianca Ziliotto, Nino Herve, A. Giannotti, S. Micera

引用次数: 1

Edge AI-Based Closed-Loop Peripheral Nerve Stimulation System for Gait Rehabilitation after Spinal Cord Injury 基于边缘人工智能的闭环周围神经刺激系统用于脊髓损伤后步态康复

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123734

Ahnsei Shon, Alex Stefanov, M. Hook, Hangue Park

{"title":"Edge AI-Based Closed-Loop Peripheral Nerve Stimulation System for Gait Rehabilitation after Spinal Cord Injury","authors":"Ahnsei Shon, Alex Stefanov, M. Hook, Hangue Park","doi":"10.1109/NER52421.2023.10123734","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123734","url":null,"abstract":"Recently, it has been revealed that Vsx2 neurons in the lumbar of the spinal cord are essential to restore locomotor function after spinal cord injury (SCI). However, Vsx2 neurons still need afferent feedback from peripheral nerves for locomotor rehabilitation. Also, movement-dependent electrical stimulation after SCI can solidify synaptic connections in a directed way. Based on these two facts, we hypothesized that providing movement-dependent electrical stimulation to hindlimb nerves may facilitate the rehabilitation process for restoring locomotion function after SCI while preventing aberrant remodeling of denervated spinal circuits. To evaluate our hypothesis, we developed an edge artificial intelligence (AI)-based closed-loop peripheral nerve stimulation system which can timely generate stimulation pulses for distal-tibial and peroneal nerves based on stance phase detection. The main parts of the system consist of a multi-site EMG electrode, neural amplifiers, an edge AI processing circuit, and neural stimulators. Medial gastrocnemius (MG) electromyography (EMG) was used as a input source of the AI model to detect the stance phase. The AI model was deployed on a dual-core 32-bit microprocessor. The whole system was evaluated with two SCI rats walking bipedally on a motorized treadmill. The accuracy of the presented AI model was calculated as 97.19%. In the animal experiments with SCI rats, stimulation pulses for the distal-tibial and peroneal nerves were timely generated for 200 ms and 100 ms, respectively based on the stance phase detected by the AI model. The experimental results suggest that the presented system can be a powerful neural interface tool to investigate the efficacy of edge AI-based closed-loop peripheral nerve stimulation on restoring locomotion function after SCI.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128087439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Discriminative Domain Adaptation and Transformers for EEG-based Cross-Subject Emotion Recognition 基于脑电图的跨主体情感识别的对抗性判别域自适应和变换

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123837

Shadi Sartipi, M. Çetin

引用次数: 2

Pilot Performance of a Chronic Intraneural Auditory Neuroprosthesis in Felines 猫科动物慢性神经内听觉神经假体的飞行员表现

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123798

W. M. Thomas, R. Gurgel, D. J. Warren

{"title":"Pilot Performance of a Chronic Intraneural Auditory Neuroprosthesis in Felines","authors":"W. M. Thomas, R. Gurgel, D. J. Warren","doi":"10.1109/NER52421.2023.10123798","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123798","url":null,"abstract":"Auditory restoration for the hearing impaired is rapidly evolving through the use of implantable stimulation devices. Despite being the current state of the art, cochlear implants (CIs) have many limitations, including low stimulation electrode independence, a need for high stimulation currents, and the inability to reliably recruit low-frequency transducing fibers in the spiral ganglion. These drawbacks stem partly from the implant location, which is electrically separated from its spiral ganglion targets. Placement of an intraneural electrode array like the Utah Slanted Electrode Array (USEA) directly in cranial nerve VIII (CN VIII) could alleviate some of these constraints. However, all prior studies on USEAs for auditory restoration have been confined to acute evaluations, none lasting longer than 56 hours. In this abstract, we present the first use of the USEA as a chronic intraneural auditory neuroprosthesis, evaluate its performance over four months, and compare its performance to a comparable acute implant. We show stable electrophysiological signals tied to the activation of the auditory transduction pathway and impedance measures of the electrodes, both of which demonstrate a stable and functional chronic device. We also compare imaging taken between an acute functional implant and the chronic implant to compare similarities in the devices' locations and orientations in light of their functionality.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying Subjects with PFC Lesions from Healthy Controls during Working Memory Encoding via Graph Convolutional Networks 工作记忆编码中PFC损伤与健康对照的图卷积网络分类

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123793

Sai Sanjay Balaji, K. Parhi

{"title":"Classifying Subjects with PFC Lesions from Healthy Controls during Working Memory Encoding via Graph Convolutional Networks","authors":"Sai Sanjay Balaji, K. Parhi","doi":"10.1109/NER52421.2023.10123793","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123793","url":null,"abstract":"This paper describes a group-level classification of 14 patients with prefrontal cortex (pFC) lesions from 20 healthy controls using multi-layer graph convolutional networks (GCN) with features inferred from the scalp EEG recorded from the encoding phase of working memory (WM) trials. We first construct undirected and directed graphs to represent the WM encoding for each trial for each subject using distance correlation- based functional connectivity measures and differential directed information-based effective connectivity measures, respectively. Centrality measures of betweenness centrality, eigenvector centrality, and closeness centrality are inferred for each of the 64 channels from the brain connectivity. Along with the three centrality measures, each graph uses the relative band powers in the five frequency bands - delta, theta, alpha, beta, and gamma- as node features. The summarized graph representation is learned using two layers of GCN followed by mean pooling, and fully connected layers are used for classification. The final class label for a subject is decided using majority voting based on the results from all the subject's trials. The GCN-based model can correctly classify 28 of the 34 subjects (82.35% accuracy) with undirected edges represented by functional connectivity measure of distance correlation and classify all 34 subjects (100% accuracy) with directed edges characterized by effective connectivity measure of differential directed information.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123980531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Motor Neuroprosthesis on Forelimb Function Recovery of Chronic Stroke Rats 运动神经假体对慢性脑卒中大鼠前肢功能恢复的影响

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123761

Huan Gao, Xiang Gao, Changjie Wang, Chaonan Yu, Kedi Xu

{"title":"Motor Neuroprosthesis on Forelimb Function Recovery of Chronic Stroke Rats","authors":"Huan Gao, Xiang Gao, Changjie Wang, Chaonan Yu, Kedi Xu","doi":"10.1109/NER52421.2023.10123761","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123761","url":null,"abstract":"The chronic upper limb motor dysfunction caused by ischemic stroke not only seriously affected the quality of patients' life but also increased the burden on society. Current physical training and transcranial neuromodulation performed not well enough on fine movement recovery. Motor neuroprosthesis emerging as a new rehabilitation technology has been proven to not only effectively improve fine upper and lower limbs' motor dysfunction caused by spinal cord injury or trauma but also affect neuroplasticity. However, this technology is still in its infancy in stroke rehabilitation. The obstacle to the application of motor neuroprosthesis in stroke rehabilitation is a proper neuroprosthetic system that can accurately decode motor-related intention or movement and deliver feedback stimulation quickly. In this work, we built a neuroprosthetic system based on motor intention decoding using local field potentials from the ipsilateral premotor cortex and simultaneously delivering epidural electrical stimulation over the ischemic area. Then we applied it to stroke rats with chronic forelimb dysfunction to verify the effect of rehabilitation. Single-pellet retrieval task was used for quantifying the forelimb fine movement. After comparing with continuous stimulation and only physical training treatment, our results primarily proved that motor neuroprosthesis can effectively improve the forelimb function of stroke, which provided a potential for clinical application.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128764195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coil Size and Current Pulse Optimization through Multi-Scale Modeling for Repetitive Transcranial Magnetic Stimulation (rTMS) 重复经颅磁刺激(rTMS)线圈尺寸和电流脉冲多尺度建模优化

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123726

Shaghayegh Abbasi, Benjamin Joray, Kenneth Rudnicki, Vincent Leung, P. Asbeck, M. Makale

{"title":"Coil Size and Current Pulse Optimization through Multi-Scale Modeling for Repetitive Transcranial Magnetic Stimulation (rTMS)","authors":"Shaghayegh Abbasi, Benjamin Joray, Kenneth Rudnicki, Vincent Leung, P. Asbeck, M. Makale","doi":"10.1109/NER52421.2023.10123726","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123726","url":null,"abstract":"Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive treatment modality utilized to treat several mental disorders including drug resistant depression, PTSD, and autism. In rTMS treatment, rapidly varying electric current is passed through a coil positioned in the vicinity of the skull. This creates a rapidly changing magnetic field, which in turn induces an electric field in the cortex, influencing neural activity. Although great progress has been made in utilizing rTMS in the past few years, the exact underlying mechanisms and optimal operating parameters are still uncertain. As a result, investigating the effects of treatment parameters on stimulation efficacy is critical. In this work we investigate the effect of rTMS system parameters on the threshold of neural stimulation, with the goal of creating a compact battery-powered rTMS system to increase treatment accessibility. These parameters include coil properties such as size and shape, as well as electric current properties such as pulse shape and pulse width. A newly developed modeling toolbox called Neural Modeling for TMS (NeMo-TMS) is utilized. The control tools added to NeMo-TMS by our team allow for finding the stimulation threshold for several pulse shapes and widths, as well as coil sizes. The simulation results can be a guideline for the coil size and optimum current pulse shape and width in a miniaturized rTMS system.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global classification of intentional movement across upper limb myoelectric pattern recognition-controlled prosthesis users 上肢肌电模式识别控制假肢使用者意向运动的整体分类

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123801

N. Stambaugh, Zachary A. Wright

{"title":"Global classification of intentional movement across upper limb myoelectric pattern recognition-controlled prosthesis users","authors":"N. Stambaugh, Zachary A. Wright","doi":"10.1109/NER52421.2023.10123801","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123801","url":null,"abstract":"One idealized vision of advanced upper limb prosthetic control is a plug-and-play design that any new user can don and instantly control attached devices as intended. Traditional body-powered prostheses are likely the closest available option but are limited in their range of motion and can have negative long-term impacts. Basic dual-site myoelectric-controlled prostheses require only minor adjustments prior to users being able to control their prosthetic device, but still requires training to learn despite only having a limited number of motions available. This contrasts with state-of-the-art myoelectric pattern recognition-controlled prostheses where a machine learning algorithm also learns the individual user; specifically, their unique patterns of muscle activity corresponding to prosthesis motions. However, the wide variation in muscle activity patterns both within and between users, mainly due to physiological differences, has been the primary reason why it is difficult to develop a true off-the-shelf prosthesis component for myoelectric pattern recognition control. In this paper, we take a small step towards this vision by investigating statistical and machine learning methods that classify prosthesis motion for any pattern recognition user. Specifically, we use a large dataset of EMG training data collected from 191 users over a six-month period to develop, as a first step, a binary classifier built to simply identify intended motion or no motion for all users. Our results could have an immediate impact on prosthesis performance for current users and justify further development of a potential global classification model which can be used by any persons with upper limb difference who wish to use a myoelectric-controlled prosthesis.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wearable-based Pain Assessment in Patients with Adhesive Capsulitis Using Machine Learning 使用机器学习的可穿戴设备对粘连性囊炎患者进行疼痛评估

2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2023-04-24 DOI: 10.1109/NER52421.2023.10123790

Chih-Hsing Chen, Kai Liu, Ting-Yang Lu, Chih-Ya Chang, Chia-Tai Chan, Yu Tsao

{"title":"Wearable-based Pain Assessment in Patients with Adhesive Capsulitis Using Machine Learning","authors":"Chih-Hsing Chen, Kai Liu, Ting-Yang Lu, Chih-Ya Chang, Chia-Tai Chan, Yu Tsao","doi":"10.1109/NER52421.2023.10123790","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123790","url":null,"abstract":"Reliable shoulder function and pain assessment tools are critical for managing patients with adhesive capsulitis (AC). Particularly, objective pain assessment plays an important role, which could support just-in-time treatment or intervention, monitor short-term and temporal dynamic within-person changes, and provide real-time feedback. Currently, pain assessment for AC still relies on a self-report approach that often suffers issues in substantial recall biases, social desirability, and measurement error. To augment typical self-report for clinical decision-making and treatment in AC, the present pilot study proposed a novel pain assessment tool using wearable inertial measurement units (IMUs) and machine learning (ML) approaches. Twenty-three patients with AC performed 5 shoulder tasks and reported pain scores based on the shoulder pain and disability index. Two wearable IMUs were placed on the wrist and arm to collect upper limb movement signals while performing shoulder tasks. We analyzed correlations between pain scores and IMU feature categories (e.g., smoothness, power, and speed). The results revealed that smoothness-related features exhibited higher Spearman correlations with patient-reported pain scores than power and speed features. Meanwhile, we built pain prediction models with the extracted IMU features and different ML approaches. The ML-based pain prediction model using Gaussian process regression showed strong and significant Spearman correlations $(boldsymbol{0.795},boldsymbol{p} < boldsymbol{0.01})$, with a mean absolute error of 5.680 and root mean square error of 6.663.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123501002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0