Journal of neural engineering最新文献

{"title":"Optogenetic neural spheroids excite primary neural network.","authors":"Saeedur Rahman, Md Saddam Hossain Joy, M Taher A Saif","doi":"10.1088/1741-2552/ade28d","DOIUrl":"10.1088/1741-2552/ade28d","url":null,"abstract":"<p><p><i>Objective.</i>Optical stimulation of<i>in vitro</i>neurons requires prior transfection with light gated ion channels. This additional step brings complexity and requires optimization. Simplification of the process will ease the undertaking of studies on biological neural networks needing external stimulation.<i>Approach.</i>We constructed a simple platform where embryonic stem cell derived optogenetic neural spheroids, cultured and maintained separately, can be seeded on top of the primary non-optogenetic neuron cultures.<i>Main results.</i>We found that the primary neural network can be stimulated through the spheroids. This allows making investigations like network response dynamics and pharmacological perturbations possible.<i>Significance.</i>Thus, our platform provides an on-demand method to stimulate neural preparations for many different studies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259624","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":"Filtered point processes tractably capture rhythmic and broadband power spectral structure in neural electrophysiological recordings.","authors":"Patrick F Bloniasz, Shohei Oyama, Emily P Stephen","doi":"10.1088/1741-2552/ade28b","DOIUrl":"10.1088/1741-2552/ade28b","url":null,"abstract":"<p><p><i>Objective</i>. Neural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. While rhythms in various diseases and brain states continue to be well studied, researchers only recently have systematically studied broadband effects in the power spectrum. Broadband effects include shifts in power across all frequencies, which correlate with changes in local firing rates, and changes in the overall shape of the power spectrum, such as the spectral slope. Shape changes are evident in various conditions and brain states, influenced by factors such as excitation-to-inhibition balance, age, and diseases; additionally, it is increasingly recognized that broadband and rhythmic effects can interact on a sub-second timescale. As such, modeling tools that explicitly deal with both rhythmic and broadband contributors to the power spectrum and capture their interactions are essential to improving the interpretability of power spectral effects.<i>Approach</i>. Here, we introduce a tractable stochastic forward modeling framework designed to capture both narrowband and broadband spectral effects when prior knowledge about the primary biophysical processes involved is available. Population-level neural recordings are modeled as the sum of filtered point processes (FPPs), each representing the contribution of a different biophysical process such as action potentials or postsynaptic potentials.<i>Main results</i>. Our approach builds on prior neuroscience FPP work by allowing multiple interacting processes, time-varying firing rates, and deriving theoretical power spectra and cross-spectra. We demonstrate several properties of the models, including that they divide the power spectrum into frequency ranges dominated by rhythmic and broadband effects and capture spectral effects across multiple timescales, including sub-second cross-frequency coupling.<i>Significance</i>. The framework can interpret empirically observed power spectra and cross-frequency coupling effects in biophysical terms, bridging theoretical models and experimental results.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259706","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":"Speech imagery brain-computer interfaces: a systematic literature review.","authors":"A Tates, A Matran-Fernandez, S Halder, I Daly","doi":"10.1088/1741-2552/ade28e","DOIUrl":"10.1088/1741-2552/ade28e","url":null,"abstract":"<p><p><i>Objective:</i>Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain-Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines.<i>Approach</i>. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode SI from neural activity.<i>Main results</i>. Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions.<i>Significance</i>SI is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259625","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":"The class imbalance problem in automatic localization of the epileptogenic zone for epilepsy surgery: a systematic review.","authors":"Valentina Hrtonova, Kassem Jaber, Petr Nejedly, Elizabeth R Blackwood, Petr Klimes, Birgit Frauscher","doi":"10.1088/1741-2552/ade28c","DOIUrl":"10.1088/1741-2552/ade28c","url":null,"abstract":"<p><p><i>Objective.</i>Accurate localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery, but the class imbalance of epileptogenic vs. non-epileptogenic electrode contacts in intracranial electroencephalography (iEEG) data poses significant challenges for automatic localization methods. This review evaluates methodologies for handling the class imbalance in EZ localization studies that use machine learning (ML).<i>Approach.</i>We systematically reviewed studies employing ML to localize the EZ from iEEG data, focusing on strategies for addressing class imbalance in data handling, algorithm design, and evaluation.<i>Results.</i>Out of 2,128 screened studies, 35 fulfilled the inclusion criteria. Across the studies, the iEEG contacts annotated as epileptogenic prior to automatic localization constituted a median of 18.34% of all contacts. However, many of these studies did not adequately address the class imbalance problem. Techniques such as data resampling and cost-sensitive learning were used to mitigate the class imbalance problem, but the chosen evaluation metrics often failed to account for it.<i>Significance.</i>Class imbalance significantly impacts the reliability of EZ localization models. More comprehensive management and innovative approaches are needed to enhance the robustness and clinical utility of these models. Addressing class imbalance in ML models for EZ localization will improve both the predictive performance and reliability of these models.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259626","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":"Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements.","authors":"L Alkhoury, G Scanavini, S Louviot, A Radanovic, S A Shah, N J Hill","doi":"10.1088/1741-2552/ade566","DOIUrl":"10.1088/1741-2552/ade566","url":null,"abstract":"<p><p><i>Objective</i>. We present a novel and lightweight method that removes ocular artifacts from electroencephalography (EEG) recordings while demanding minimal training data.<i>Approach</i>. A robust, cross-validated thresholding procedure automatically detects the times at which eye blinks occur, then a linear scalp projection is estimated by regressing a simplified time-locked reference signal against the multi-channel EEG.<i>Main results</i>. Performance was compared against four commonly-used and readily available blink removal methods: signal subspace projection and forward regression (Reg) from the MNE toolbox, EEGLab's independent component analysis (ICA) combined with ICLabel for automated component identification, and Artifact Subspace Reconstruction (ASR) Python implementation compatible with MNE. On semi-synthetic blink-contaminated EEG data, our method exhibited better reconstruction of the ground truth than the two MNE native methods, and comparable (or better in some scenarios) performance to ASR algorithm and ICA+IClabel. We also examined a real EEG dataset from 16 human participants, where the ground truth was unknown. Our method affected contaminated channels in blink intervals more than the two MNE native methods and ASR, while having a smaller impact on non-blink intervals, uncontaminated channels, and higher-frequency amplitudes, than the two MNE methods; its performance was again similar to ICA+ICLabel. On a second real dataset from 42 human participants, we showed that ARMBR removed the unwanted blink artifacts while successfully preserving the desired event-related-potential signals.<i>Significance</i>. The proposed algorithm exhibited comparable, and in some scenarios better performance relative to readily-available implementations of other widely-used methods. Another feature of our method is its potential as method for online applications. Therefore, it stands to make valuable contributions towards the automation of neural-engineering technologies and their translation from laboratory to clinical and other real-world usage.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318987","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":"Projection-based numerical optimization of transcranial magnetic coil placement for nonconvex target surfaces and double-cone coil geometries.","authors":"Xu Zhang, Roeland Hancock, Sabato Santaniello","doi":"10.1088/1741-2552/ade18b","DOIUrl":"10.1088/1741-2552/ade18b","url":null,"abstract":"<p><p><i>Objective.</i>To develop a coil placement optimization pipeline for transcranial magnetic stimulation (TMS) that improves over existing solutions by guaranteeing the feasibility of the solution when double-cone coils are used and/or targets are placed over nonconvex scalp areas like the occipital region.<i>Approach.</i>Our proposed pipeline estimates feasible candidate coil locations by projecting the coil's geometry over the scalp around the target site and optimizing the coil's orientation to maximize scalp exposure to coil while avoiding coil-scalp collision. Then, the reciprocity principle is used to select the best position/orientation among candidates and maximize the average electric field (E-field) intensity at the target site. Our pipeline was tested on five magnetic resonance imaging-derived human head models for three different targets (motor cortex, lateral cerebellum, and cerebellar inion) and four coil models (planar coil: MagStim D70; double-cone coils: MagStim DCC, MagVenture Cool-D-B80, and Deymed 120BFV).<i>Main results.</i>Our pipeline returned several feasible solutions for any combination of anatomical target and coil, calculated and screened over 2000 candidates in minutes, and resulted in optimal locations that satisfy the minimum coil-scalp distance, whereas the direct method returned feasible candidates for just one combination of target and coil, i.e. planar coil and convex target over the motor cortex. We also found that, when the objective is to maximize the E-field magnitude, the target-to-scalp extension line is a better axis for coil translation compared to the normal vector at the scalp's surface, which is commonly used in existing approaches.<i>Significance.</i>We expand the use of numerical optimization for coil placement to double-cone coils, which are rapidly diffusing in research and clinical settings, and novel application domains, e.g. cerebellar TMS and ataxia treatment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144236306","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":"Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.","authors":"Fangzhou Xu, Yanbing Liu, Yanzi Li, Chao Zhang, Zhe Han, Tianzheng He, Xiaolin Xiao, Feng Chao, Jiancai Leng, Minpeng Xu","doi":"10.1088/1741-2552/ade829","DOIUrl":"https://doi.org/10.1088/1741-2552/ade829","url":null,"abstract":"<p><p>With the rapid development of Brain-Computer Interface (BCI) technology, Steady-State Visual Evoked Potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved Filter Bank Dual-frequency Task-Discriminant Component Analysis (FBD-TDCA) algorithm. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. A 12-target brain-controlled unmanned vehicle online simulation with 12 participants further validated the proposed paradigm and algorithm. In the binocular stimulation paradigm, the average Information Transfer Rate (ITR) reached 154.67±19.69 bits/min in online experiments, with offline training yielding an ITR of 170.7±31.2 bits/min. This novel stimulation paradigm not only supports large-scale target sets for BCI systems but also improves visual comfort, offering stability and feasibility for practical brain-controlled applications.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499964","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":"P300-based brain-computer interface for communication in assistive technology centres: influence of users' profile on BCI access.","authors":"Galiotta Valentina, Caracci Valentina, Toppi Jlenia, Pichiorri Floriana, Colamarino Emma, Cincotti Febo, Mattia Donatella, Riccio Angela","doi":"10.1088/1741-2552/addf7f","DOIUrl":"10.1088/1741-2552/addf7f","url":null,"abstract":"<p><p><i>Objective</i>. Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A brain-computer interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-centre to investigate their eligibility for BCI access and the factors influencing the BCI control.<i>Approach</i>. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance.<i>Main results</i>. The 7.1% of the users controlled the system with a mean accuracy of 93.6 ± 8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR.<i>Significance</i>. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centres, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210528","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":"Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography.","authors":"Harshavardhana T Gowda, Zachary D McNaughton, Lee M Miller","doi":"10.1088/1741-2552/ade7af","DOIUrl":"https://doi.org/10.1088/1741-2552/ade7af","url":null,"abstract":"<p><strong>Objective: </strong>In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced damage (e.g., from radiotherapy) to the speech articulators.&#xD;&#xD;Approach.&#xD;To achieve this, we collect EMG signals from the face, jaw, and neck as subjects articulate speech, and we perform EMG-to-speech translation. &#xD;&#xD;Main results.&#xD;Our findings reveal that the manifold of symmetric positive definite (SPD) matrices serves as a natural embedding space for EMG signals. Specifically, we provide an algebraic interpretation of the manifold-valued EMG data using linear transformations, and we analyze and quantify distribution shifts in EMG signals across individuals. &#xD;&#xD;Significance.&#xD;Overall, our approach demonstrates significant potential for developing neural networks that are both data- and parameter-efficient-an important consideration for EMG-based systems, which face challenges in large-scale data collection and operate under limited computational resources on embedded devices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487574","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":"Patch Clamp Recordings of Action Potentials from Pyramidal Neuron in Hippocampus CA1 Under Focused Ultrasound Neurostimulation with MEMS Self-Focusing Acoustic Transducer.","authors":"Jaehoon Lee, Yongkui Tang, Akash Roy, Kianoush Sadeghian Esfahani, Su-Youne Chang, Eun Sok Kim","doi":"10.1088/1741-2552/ade7ae","DOIUrl":"https://doi.org/10.1088/1741-2552/ade7ae","url":null,"abstract":"<p><p>This paper reports the extensive recordings of the action potentials in a pyramidal cell in the CA1 region of the rat's hippocampus using the whole-cell patch clamp technique, showing the modulation of the spike activities induced by the non-thermal focused ultrasound (FUS). A Self-focused acoustic transducer (SFAT) is designed and fabricated on a thin and translucent 127-µm lead zirconate titanate (PZT) substrate, which allows the infra-red (IR) light to pass through so that the cells in the hippocampus (over the transducer) may be viewed from the top for the patch-clamp experiment. This setup enables real-time recording of action potentials from a single neuron and monitoring of its neuronal activity modulation induced by FUS. The SFAT operates at 18.4 MHz and generates Low-Intensity Focused Ultrasound (LIFU), having a focal size of 46 µm in diameter at a distance of 400 µm from the surface of the transducer. Three different types of SFAT, an active SFAT, a FUS-blocking control SFAT, and a low-EMI (electromagnetic interference) SFAT are designed to study the thermal effect and the EMI effect of the transducer. The SFAT is operated under 48 different combinations of acoustic parameters, including peak-to-peak intensity, pulse repetitive frequency (PRF), and pulse duration, applied across78 tissuesamples. The results indicate that the FUS is capable of modulating thebrain's neuronal activities bidirectionally (inhibition or excitation) depending on the acoustic parameters. Optimal parameters for achieving the inhibition effect are 60 Vpp(ISPPA= 2.11 W/cm2), 35 kCycles/pulse (pulse duration = 1.90ms), with a PRF of 100Hz, yielding a 60% success rate. When operating SFAT at 120 Vpp(ISPPA= 8.44W/cm2), 50 kCycles/pulse (pulse duration = 2.72ms) and a PRF of 20 Hz, neuronal excitation could be achieved with a 60% success rate.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487575","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}