Journal of neural engineering最新文献

{"title":"Prediction of impedance characteristic during electrical stimulation with microelectrode arrays.","authors":"Andreas Erbslöh, Julius Zimmermann, Sven Ingebrandt, W Mokwa, Karsten Seidl, Ursula van Rienen, Gregor Schiele, Rainer Kokozinski","doi":"10.1088/1741-2552/adc2d5","DOIUrl":"https://doi.org/10.1088/1741-2552/adc2d5","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays.</p><p><strong>Approach: </strong>We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established.&#xD;Main results.&#xD;We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transformbased impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable new functionalities in future MEA-based neural devices For example, adaptive electrical stimulation or (re-)selection of recording electrodes can be supported by taking the actual state of the electrode into account.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665741","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":"Detection of freely moving thoughts using SVM and EEG signals.","authors":"Sairamya Nanjappan Jothiraj, Caitlin Mills, Zachary C Irving, Julia W Y Kam","doi":"10.1088/1741-2552/adbd77","DOIUrl":"10.1088/1741-2552/adbd77","url":null,"abstract":"<p><p><i>Objective.</i>Freely moving thought is a type of thinking that shifts from one topic to another without any overarching direction or aim. The ability to detect when freely moving thought occurs may help us promote its beneficial outcomes, such as for creative thinking and positive mood. Thus far, no studies have used machine learning to detect freely moving thought on the basis of 'objective' (e.g. neural or behavioral) data.<i>Approach.</i>Our study addresses this gap, using event-related potential (ERP) and spectral features of electroencephalogram (EEG) signals as well as behavioral measures during a simple attention task and machine learning to detect freely moving thought. EEG features were first examined with both inter-subject and intra-subject strategies. Specifically, the statistical and entropy features of the P3 ERP and alpha spectral measures were entered as inputs to the support vector machine. The best combination of EEG features achieving higher classification performance in both strategies were then selected to combine with behavioral features to further enhance classification performance.<i>Main results.</i>Our best performing model has a Matthew's correlation coefficient and area under the curve of 0.3105 and 0.6665 for inter-subject models and 0.2815 and 0.6407 for intra-subject models respectively.<i>Significance.</i>The above chance level performance in both strategies using EEG and behavioral features shows great promise for machine learning approaches to detect freely moving thought and highlights their potential for real-time prediction in the real world. This has important implications for enhancing creative processes and mood associated with freely moving thought.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574990","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":"Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.","authors":"Taylor G Hobbs, Charles M Greenspon, Ceci Verbaarschot, Giacomo Valle, Christopher Lee Hughes, Michael L Boninger, Sliman J Bensmaia, Robert A Gaunt","doi":"10.1088/1741-2552/adc2d4","DOIUrl":"https://doi.org/10.1088/1741-2552/adc2d4","url":null,"abstract":"<p><strong>Objective: </strong>Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts.</p><p><strong>Approach: </strong>In this study, we investigated whether biomimetic stimulation patterns - ICMS patterns that reproduce essential features of natural neural activity - increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation.</p><p><strong>Main results: </strong>We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation.</p><p><strong>Significance: </strong>We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665740","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":"Parameter-dependent cell-type specific effects of transcranial focused ultrasound stimulation in an awake head-fixed rodent model.","authors":"Sandhya Ramachandran, Huan Gao, Eric Yttri, Kai Yu, Bin He","doi":"10.1088/1741-2552/adbb1f","DOIUrl":"10.1088/1741-2552/adbb1f","url":null,"abstract":"<p><p><i>Objective.</i>Transcranial focused ultrasound (tFUS) is a promising neuromodulation technique able to target shallow and deep brain structures with high precision. Previous studies have demonstrated that tFUS stimulation responses are cell-type specific, and specifically tFUS can elicit time-locked neural activity in regular spiking units (RSUs) that is sensitive to increases in pulse repetition frequency (PRF), while time-locked responses are not seen in fast spiking units (FSUs). These findings suggest a unique capability of tFUS to alter circuit network dynamics with cell-type specificity; however, these results could be biased by the use of anesthesia, which significantly modulates neural activities.<i>Approach.</i>In this study, we developed an awake head-fixed rat model specifically designed for simultaneous tFUS stimulation using a customized 128-element ultrasound array transducer, and recording of spiking data. Using this novel animal model, we examined a series of PRFs and burst duty cycles (DCs) to determine their effects on neuronal subpopulations without anesthesia.<i>Main results.</i>We observed cell type specific responses to varying PRF and DC in the awake setting as well as the anesthetized setting, with time locked responses observed in RSU and delayed responses in FSU. Anesthesia broadly was found to dampen responses to tFUS, and affected the latency of delayed responses. Preferred parameters for inducing time-locked responses appear to be 1500 Hz PRF and 60% DC.<i>Significance.</i>We conclude that despite some differences in response, isoflurane anesthesia is not a major confound in studying the cell-type specificity of ultrasound neuromodulation, but may affect studies of circuit dynamics and FSU. Our developed awake model will allow for future investigations without this confound.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of source-level motor imagery classification for people with multiple sclerosis.","authors":"John S Russo, Thomas A Shiels, Chin-Hsuan Sophie Lin, Sam E John, David B Grayden","doi":"10.1088/1741-2552/adbec1","DOIUrl":"10.1088/1741-2552/adbec1","url":null,"abstract":"<p><p><i>Objective.</i>There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.<i>Approach.</i>Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.<i>K</i>-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.<i>Main Results.</i>Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.<i>Significance.</i>This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598624","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":"An active electronic, high-density epidural paddle array for chronic spinal cord neuromodulation.","authors":"Samuel R Parker, Jonathan S Calvert, Radu Darie, Jaeson Jang, Lakshmi Narasimhan Govindarajan, Keith Angelino, Girish Chitnis, Yohannes Iyassu, Elias Shaaya, Jared S Fridley, Thomas Serre, David A Borton, Bryan L McLaughlin","doi":"10.1088/1741-2552/adba8b","DOIUrl":"10.1088/1741-2552/adba8b","url":null,"abstract":"<p><p><i>Objective</i>. Epidural electrical stimulation (EES) has shown promise as both a clinical therapy and research tool for studying nervous system function. However, available clinical EES paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic delivery device, limiting the treatment area or density of epidural electrode arrays. We aimed to eliminate this burden using advanced on-paddle electronics.<i>Approach</i>. We developed a smart EES paddle with a 60-electrode programmable array, addressable using an active electronic multiplexer embedded within the electrode paddle body. The electronics are sealed in novel, ultra-low profile hermetic packaging. We conducted extensive reliability testing on the novel array, including a battery of ISO 10993-1 biocompatibility tests and determination of the hermetic package leak rate. We then evaluated the EES device<i>in vivo</i>, placed on the epidural surface of the ovine lumbosacral spinal cord for 15 months.<i>Main results.</i>The active paddle array performed nominally when implanted in sheep for over 15 months and no device-related malfunctions were observed. The onboard multiplexer enabled bespoke electrode arrangements across, and within, experimental sessions. We identified stereotyped responses to stimulation in lower extremity musculature, and examined local field potential responses to EES using high-density recording bipoles. Finally, spatial electrode encoding enabled machine learning models to accurately perform EES parameter inference for unseen stimulation electrodes, reducing the need for extensive training data in future deep models.<i>Significance</i>. We report the development and chronic large animal<i>in vivo</i>evaluation of a high-density EES paddle array containing active electronics. Our results provide a foundation for more advanced computation and processing to be integrated directly into devices implanted at the neural interface, opening new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11920892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rehabilitation with hybrid assistive limb improves upper limb paralysis in patients with cerebral hemorrhage by repairing axonal injury of the corticospinal tract.","authors":"Masahiko Nishimura, Shigetaka Kobayashi, Tomomi Kuninaka, Yohei Hokama, Hideki Nagamine, Shogo Ishiuchi","doi":"10.1088/1741-2552/adbe3c","DOIUrl":"https://doi.org/10.1088/1741-2552/adbe3c","url":null,"abstract":"<p><p><i>Objective.</i>Effective rehabilitation for upper limb paralysis in patients with intracerebral hemorrhage mediated by hemiplegia has not yet been established. We evaluated the effectiveness of upper limb functional training using a wearable-type exoskeleton driven by bio-electric signals using upper limb motor function scores and tractography of the corticospinal tract (CST).<i>Approach.</i>Nine patients with putamen and seven with thalamus hemorrhage were trained using the hybrid assistive limb (HAL) of the wearable exoskeleton. Among the participants, 12 individuals were patients with severe hemiplegic, indicated by a Fugl-Meyer assessment (FMA) score of 10. We also investigated the relationship between improvement in upper limb function and changes in mean diffusivity, axial diffusivity, radial diffusivity (RD), or fractional anisotropy (FA) in the CST.<i>Main results.</i>Following HAL training, upper limb function scores increased in all patients. We observed a clinically significant improvement in nine patients, with a mean effect size of 26 ± 12.7. HAL training was effective in improving upper limb function in patients with an FA ratio (the affected/unaffected side) ⩾0.86 in the CST. Patients with clinically significant improvements had a mean 16 ± 15% increase in FA ratio in the CST. Patients with greater improvement in upper-limb function tended to have lower RD values in the CST, and the effect size of the RD value and FMA was demonstrated to be negatively correlated (<i>rs</i>= -0.54). An increase in FA ratio and a decrease in RD values in the CST of the cerebral peduncle are significant findings that suggest improvements in upper limb function.<i>Significance.</i>These findings highlight the effectiveness of HAL training in improving upper-limb dysfunction in patients with subacute cerebral hemorrhage. Improvement of upper limb function by assistive actuation with the wearable exoskeleton based on bio-electric signals may be caused by the promotion of the restoration of white matter integrity of the CST.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652897","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":"Less is more: selection from a small set of options improves BCI velocity control.","authors":"Pedro I Alcolea, Xuan Ma, Kevin Bodkin, Lee E Miller, Zachary C Danziger","doi":"10.1088/1741-2552/adbcd9","DOIUrl":"10.1088/1741-2552/adbcd9","url":null,"abstract":"<p><p><i>Objective.</i>Decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities.<i>Approach</i>. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 naïve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF).<i>Main Result</i>. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per visit compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF.<i>Significance</i>. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569220","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":"Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome.","authors":"Ling Ding, Qingyu Zou, Junming Zhu, Yueming Wang, Yuxiao Yang","doi":"10.1088/1741-2552/adba8d","DOIUrl":"10.1088/1741-2552/adba8d","url":null,"abstract":"<p><p><i>Objective</i>. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.<i>Approach</i>. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.<i>Main results</i>. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.<i>Significance</i>. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517740","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":"Optimal placement of high-channel visual prostheses in human retinotopic visual cortex.","authors":"Rick van Hoof, Antonio Lozano, Feng Wang, P Christiaan Klink, Pieter R Roelfsema, Rainer Goebel","doi":"10.1088/1741-2552/adaeef","DOIUrl":"10.1088/1741-2552/adaeef","url":null,"abstract":"<p><p><i>Objective.</i>Recent strides in neurotechnology show potential to restore vision in individuals with visual impairments due to early visual pathway damage. As neuroprostheses mature and become available to a larger population, manual placement and evaluation of electrode designs become costly and impractical. An automatic method to simulate and optimize the implantation process of electrode arrays at large-scale is currently lacking.<i>Approach.</i>Here, we present a comprehensive method to automatically optimize electrode placement for visual prostheses, with the objective of matching predefined phosphene distributions. Our approach makes use of retinotopic predictions combined with individual anatomy data to minimize discrepancies between simulated and target phosphene patterns. While demonstrated with a 1000-channel 3D electrode array in V1, our simulation pipeline is versatile, potentially accommodating any electrode design and allowing for design evaluation.<i>Main results.</i>Notably, our results show that individually optimized placements in 362 brain hemispheres outperform average brain solutions, underscoring the significance of anatomical specificity. We further show how virtual implantation of multiple individual brains highlights the challenges of achieving full visual field coverage owing to single electrode constraints, which may be overcome by introducing multiple arrays of electrodes. Including additional surgical considerations, such as intracranial vasculature, in future iterations could refine the optimization process.<i>Significance.</i>Our open-source software streamlines the refinement of surgical procedures and facilitates simulation studies, offering a realistic exploration of electrode configuration possibilities.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054700","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}