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

{"title":"Can the crossmodal congruency task be a proxy for intuitiveness of sensory feedback in lower-limb amputees?","authors":"R. Bose, Bailey Petersen, R. Klatzky, L. Fisher","doi":"10.1109/NER52421.2023.10123872","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123872","url":null,"abstract":"The lack of sensory feedback from the foot contributes to balance impairments and falls in individuals with a lower-limb amputation. Ongoing research focuses on developing somatosensory neuroprostheses to restore sensation to the missing limb via electrical stimulation; however, participants often report that the sensations produced by the stimulation are unintuitive. The impact of sensory intuitiveness on prosthetic function has not yet been established, in part due to the lack of a reliable metric of intuitiveness. Previous work has proposed a speeded cross-modal congruency task as a means to quantify intuitiveness for upper-limb somatosensory neuroprostheses. Participants verbally indicate the location of evoked sensations while ignoring a visual distractor at the same or another location (congruent or incongruent trials, respectively). The magnitude of slowing of response times for incongruent trials, called the cross-modal congruence effect (CCE), has been used to measure intuitiveness, under the assumption that more intuitive sensations will be more intrusive. This study modified the task to evaluate the intuitiveness of two types of evoked sensations (electrotactile or pneumotactile) in the knee and foot. Fifteen able-bodied individuals completed the modified task. The CCE was higher for pneumotactile stimulation compared to electrotactile stimulation at the knee, but not at the foot. The location dependence of the CCE in the lower extremity suggests that it is not a good proxy for sensory intuitiveness in the lower-limb and thus should not be used for assessing lower-limb somatosensory neuroprostheses.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"35 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":"131895286","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}

{"title":"Training changes the EEG complexity and functional connectivity of precise timing prediction*","authors":"Jiayuan Meng, Xiaoyu Li, Yingru Zhao, Hongzhan Zhou, Minpeng Xu, Dong Ming","doi":"10.1109/NER52421.2023.10123830","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123830","url":null,"abstract":"Precise timing prediction is the ability to estimate time in millisecond timescale, it can speed up behavior, optimize perception, benefit adaptive behaviors. Behavioral studies have demonstrated training can improve the performance of precise timing prediction. However, neural evidence is still lacking in describing how training changes the neural characteristics of precise timing prediction. This study designed a cue-tapping (task1)/ timing-in-mind (task2) experiment, collected behavioral and electroencephalogram (EEG) data of 24 subjects in both before and after training period. Sample entropy (SampEn) and Lempel-Ziv complexity (LZC) were calculated to measure EEG complexity, functional connectivity based on phase locking value was also involved. Consequently, behavioral results showed that error time declined and accuracy rate increased as training progressed. SampEn was much smaller after training in almost all frequency-bands in task1, and reduced in task2 as well. LZC showed a decreased tendency after training, but no statistical significance was found. Moreover, after training, much stronger functional connectivity was found in low frequency-band in both tasks. The results can shed light on the modeling of training and precise predictive timing.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"153 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":"132212313","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}

{"title":"Wearable EEG-Based Classification of Odor-Induced Emotion","authors":"Oranatt Chaichanasittikarn, Mengting Jiang, Manuel S. Seet, Mariana Saba, Junji Hamano, Andrei Dragomir","doi":"10.1109/NER52421.2023.10123826","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123826","url":null,"abstract":"Wearable brain sensing and affective brain pro-cessing have recently seen surging interest due to advances in neurotechnologies and rapidly expanding application areas, among which consumer neuroscience, neuroergonomics and dig-ital health. Despite significant progress in understanding olfaction and affective cortical processing, several aspects related to odor-induced emotion remain to be clarified. Among these, are the feasibility of emotion classification using wearable electroen-cephalography (EEG), and the reliability of brain metrics previ-ously proposed in the context of different stimuli in cross-domain emotion recognition. In this study we investigated whether wearable EEG power spectral density (PSD) features can be used to reliably discriminate between odor-induced positive and negative emotions. To this goal, subject-independent trial data has been used within a cross-validation procedure with 3 machine learning algorithms (kNN, linear-SVM, RBF-SVM) to classify the neural response to different odor stimuli. We found that RBF-SVM and PSD features in the delta, theta, alpha and gamma bands yield a high accuracy of 86.1% in classifying positive- and negative-emotion induced by odor stimuli. Moreover, we found that brain metrics relevant for emotion-recognition in the context of other types of stimuli (such as visual) carry discriminative value also in the case of odor-induced emotion.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"20 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":"132308074","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}

{"title":"Impact of microcoil shape and the efficacy of soft magnetic material cores in focal micromagnetic neurostimulation","authors":"Renata Saha, Kai Wu, Jian‐Ping Wang","doi":"10.1109/NER52421.2023.10123859","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123859","url":null,"abstract":"Micromagnetic neurostimulation $(upmu text{MS})$, despite being in its infancy, has shown promising results in spatially selective activation of neurons. The devices are micrometer-sized coils or microcoils $(upmu mathbf{coils})$ which work on the principle of Faraday's Law of electromagnetic induction. Upon applying a time-varying current through these $upmu text{coils}$ they generate a time-varying magnetic field which in turn induces an electric field that activates the neurons. These $upmu text{coils}$ are spared from biofouling nuances as this induced electric field is not in direct electrochemical contact with the tissues. However, these $upmu mathbf{coils}$ have a high power of operation which lead to undesirable thermal effects on neurons. In this work, we have studied the efficacy of soft magnetic material (SMM) cores on these $upmu text{coils}$ to solve two existing challenges for $upmu text{MS}$. First, to minimize the power consumption for these $upmu text{coils}$. Second, to achieve even more precise and focal activation of the neural tissues. We have studied 3 shapes of $upmu text{coils}$ with comparable sizes in terms of spatial contour plots of magnetic field and induced electric field. Furthermore, the efficacy of 2 shapes of SMM cores, cone and rod, of varying sizes have been studied to obtain a spatially focal magnetic field and increased magnitude of induced electric field.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"55 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":"133381510","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}

{"title":"A Shared Resource for Building Polymer-Based Microelectrode Arrays as Neural Interfaces","authors":"K. Scholten, Huijing Xu, D. Song, E. Meng","doi":"10.1109/NER52421.2023.10123883","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123883","url":null,"abstract":"Chronic functionality of neural interfaces (NI) is hampered by the physiological response to foreign objects, in part due to the mismatch of mechanical properties between soft neural tissue and the rigid materials used in interface construction. Polymer-based NIs have emerged as a key new technology in the pursuit of chronically stable neural recording and stimulation, but most polymer NIs are bespoke devices developed as part of specific research missions; many researchers do not have access to polymer-based NIs technology and among those who do there is a severe lack of standardization in material, construction, packaging, and testing, leading to a lack of repeatability among datasets. Here we present the Polymer Implantable Electrode (PIE) Foundry, a shared-resource for fabricating and disseminating standardized polymer-based microelectrode arrays for use in NIs. The model is based on the successful shared prototyping concept developed for the field of semiconductor research. Professional staff, supported by the BRAIN Initiative funding and operating in cleanroom space provided by the University of Southern California, offer design, fabrication, packaging, and testing of polymer-based microelectrode arrays as a free service to academic and non-profit research groups. The core enabling technology is a standardized set of micromachining protocols applied to the biocompatible, thin-film polymer Parylene C. By leveraging this method, we produce microelectrode arrays of varied size, shape, channel count, and application, disseminating hundreds of arrays to 18+ research groups in our first three years of operation. By standardizing materials, fabrication, and packaging, we create repeatable and comparable devices and have built a library of shareable designs. Channel counts range from 2 to 64, electrode sizes range from 15 μm diameter to 1 mm, designs include penetrating neural probes, spinal paddle electrodes, surface arrays for electroencephalography, and peripheral nerve cuffs for recording and stimulation, animal models include songbird, mouse, rat, cat, and sheep. Here we present details of our organizational structure, fabrication and packaging methods, representative examples of ex vivo and in vivo electrode performance, and key results from the first three years of Foundry operation.","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":"130353445","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}

{"title":"EffiE: Efficient Convolutional Neural Network for Real-Time EMG Pattern Recognition System on Edge Devices","authors":"Jimmy Lu, Philip Liang, Jin Chul Rhim, Xiaorong Zhang, Zhuwei Qin","doi":"10.1109/NER52421.2023.10123741","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123741","url":null,"abstract":"With the advancement of deep learning (DL) technologies, applying DL methods to processing surface electromyo-graphic (sEMG) signals for movement intent recognition has gained increasing interest in the research community. Compared to conventional non-DL methods commonly used for EMG pattern recognition (PR), DL algorithms have the advantage of automatically extracting sEMG features without the cumbersome manual feature engineering step and are especially effective in processing sEMG signals collected from 1-dimentional (1D) or 2D sensor arrays. However, a key challenge to the deployment of DL methods in sEMG-controlled neural-machine interface (NMI) applications (e.g., myoelectric controlled prostheses) is the high computational cost associated with DL algorithms (e.g., convolutional neural network (CNN)) since most NMI applications need to be implemented on resource-constrained embedded computer systems and have real-time requirements. In this paper, we designed and implemented EffiE - an efficient CNN for real-time EMG PR system on edge devices. The development of the EffiE system integrated several strategies including a deep transfer learning strategy to adaptively and quickly update the pre-trained CNN model based on the user's newly collected data on the edge device, and a deep learning quantization method that can dramatically reduce the memory consumption and computational load of the CNN model without sacrificing the model accuracy. The proposed EffiE system has been implemented on a Sony Spresense 6-core microcontroller board as a working prototype for real-time NMIs. The embedded NMI prototype has integrated input/output interfaces as well as efficient memory management and precise timing control schemes to achieve real-time DL-based myoelectric control of a bionic arm using hand gestures. We released all the source code at: https://github.com/MIC-Laboratory/EffiE","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"30 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":"115356443","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}

{"title":"Development of a 3D Printing Strategy for Completely Polymeric Neural Interfaces Fabrication","authors":"C. Zinno, Ilaria Cedrola, A. Giannotti, E. R. Riva, S. Micera","doi":"10.1109/NER52421.2023.10123838","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123838","url":null,"abstract":"The fabrication of neural interfaces (NIs) typically relies nowadays on the implementation of complex, expensive, and time-consuming photolithographic processes. Metals and polymers are the materials currently used to fabricate NIs. Conductive polymers could be an alternative to metals to enhance the biocompatibility of the devices. Additive manufacturing techniques provide an easier and low-cost approach to process and finely tuning the geometrical and morphological features of polymers. Here, we propose a 3D printing strategy for the fabrication of completely polymeric neural interfaces, based on extrusion printing. The materials have been chosen to enhance the biocompatibility of the devices. PDMS has been chosen as insulating substrate, while a PEDOT:PSS-based ink has been selected for the conductive component. Morphological, mechanical, and rheological analyses on the inks have been carried out and a first prototype of a neural interface has been fabricated. The PDMS has a Young Modulus of 600 kPa, in the same order of magnitude as peripheral nerves, with a thickness of 160 μm. The PEDOT:PSS inks fabricated present a shear thinning behavior, ideal for an extrusion printing process This approach could represent a valuable alternative to photolithography and an innovative method for the fabrication of NIs, due to the high degree of customization, ease of implementation, low-cost and flexibility in materials choice.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"148 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":"114371561","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}

{"title":"Phase-Locked Noninvasive Brain Stimulation","authors":"Nicolò Rossetti, Roberto Garcia van der Westen, V. Mihajlović","doi":"10.1109/NER52421.2023.10123885","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123885","url":null,"abstract":"One of the unique methods to directly impact the electrical activity of the brain is through the use of noninvasive brain stimulation methods. These methods, such as transcranial current stimulation (tCS), are applied in scientific community to provide insights in cognitive processes but also to treat certain health conditions. Although transcranial alternate current stimulation (tACS) is less frequently used than widespread transcranial direct current stimulation (tDCS), it offers the possibility to directly entrain brain activity, leading to short- and potentially long-term physiological and cognitive effects. Together with methods facilitating better sensing and stimulation focus, it enables improved spatio-temporal neuromodulation. One of the crucial aspects of facilitating this is introducing a phase locking mechanism within a closed-loop, encompassing a real-time brain activity readout and targeted stimulation. In this paper we introduce a phase-locked closed-loop tACS framework and demonstrate its working principles. We focus on the phase-locking method as a crucial component and analyse its performance on an artificial setup that includes an in-house developed phantom head and a software framework. The achieved temporal precision is in the order of 5.4° ± 2.31°. The future outlook of the proposed framework is discussed, in particular looking at the research applications and clinical potential.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"53 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":"115811381","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}

{"title":"Effects of EEG Analysis Window Location on Classifying Spoken Mandarin Monosyllables","authors":"Mingtao Li, Shangdi Liao, S. Pun, Fei Chen","doi":"10.1109/NER52421.2023.10123748","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123748","url":null,"abstract":"The direct-speech brain-computer interfaces (DS-BCIs) with self-paced paradigms are much more promising and practical than indirect BCIs with general synchronous paradigms. As the exact onset and offset locations of analysis window are hard to achieve in the imagined speech of ideal DS-BCIs, spoken speech with clear audible output in this study is used as a medium to study the impact of exact location of analysis window in self-paced BCIs. This work aimed to use shifted analysis windows to simulate the situations with different levels of onset location errors of analysis window in the EEG-based classification of spoken Mandarin monosyllables carrying vowels and lexical tones. The analysis window (based on the duration of the available overt speech) was shifted from the true onset location. The Riemannian manifold method was used to extract features for the collected EEG signals, and a linear discriminant analysis (LDA) was employed to classify different vowels and lexical tones. The results in vowel and tone classifications were 70.7% and 54.9%, respectively, at an overall best-shifted level. It was found that vowel and lexical tone classifications reached their best performances at different shifting levels of analysis window. When choosing a suitable analysis window, the EEG signals without shift were more suitable to classify vowels, and those EEG signals away from the onset location were found to benefit tone classification.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"51 2 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":"116065723","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}

{"title":"Physiological Parameter Estimation for Dorsal Column Spinal Cord Stimulation","authors":"Andrew Haddock, Tianhe Zhang, R. Esteller","doi":"10.1109/NER52421.2023.10123812","DOIUrl":"https://doi.org/10.1109/NER52421.2023.10123812","url":null,"abstract":"Spinal Cord Stimulation (SCS) is an established treatment option for patients living with chronic neuropathic pain. Although recently developed implanted pulse generator (IPG) systems are utilizing real-time electrophysiological measurements of evoked compound action potential (ECAP) amplitude as a feedback signal for modulating SCS therapy, it is not clear whether this is the optimal feedback signal for maintaining consistent neural activation. In this paper, we consider ECAP amplitude alongside other extracted features, such as AUC, N1 time, and conduction velocity, and investigate how these features respond to changes in physiological parameters in a computational model of ECAPs produced by dorsal column SCS. We use a simulated test bed to compare therapy-relevant parameter estimation by linear estimators and a Kalman Filter constructed from different ECAP features, and we demonstrate that a Kalman Filter using N1 time or conduction velocity has robust performance across the range of simulated conditions at the stimulating and sensing electrodes, while estimators using ECAP amplitude and AUC features are shown to be prone to higher error when conditions at the stimulating and sensing electrodes are not ideal. These results may drive future adaptive SCS therapy developments and a reconsideration of how to leverage extracted ECAP features for detecting therapy-relevant signal changes.","PeriodicalId":201841,"journal":{"name":"2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"37 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":"115283263","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}