Journal of neural engineering最新文献

{"title":"A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography.","authors":"Zhangyu Xu, Majid Khazaee, Nhan Duy Truong, Deniel Havenga, Armin Nikpour, Arman Ahnood, Omid Kavehei","doi":"10.1088/1741-2552/ad8dfe","DOIUrl":"10.1088/1741-2552/ad8dfe","url":null,"abstract":"<p><p><i>Objective</i>. Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Currently, solutions for endovascular electrocorticography (ECoG) include a stent in the brain with sensing electrodes, a chest implant to accommodate electronic components to provide power and data telemetry, and a long (tens of centimeters) cable travel through vessels with a set of wires in between. Removing this long cable is the key to the clinical viability of eBCIS as it carries risks and limitations, especially for patients with fragile vasculature.<i>Approach</i>. This work introduces a wireless and leadless telemetry and power transfer solution for ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent, removing the need for long, intrusive cables.<i>Main results</i>. The optical telemetry achieves data transmission speeds of over 2 Mbit/s, capable of supporting 41 ECoG channels at a 2 kHz sampling rate with 24-bit resolution. The FUS power transfer system delivers up to 10 mW of power to the implant through the scalp(6 mm), skull(10 mm), and subdural space(5 mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.<i>Significance</i>. This leadless and wireless solution eliminates the need for long cables and auxiliary implants, potentially reducing complications and enhancing the clinical applicability of eBCIs. The proposed system represents a step forward in enabling safer and more effective ECoG for a broader range of patients.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565398","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":"Anchoring temporal convolutional networks for epileptic seizure prediction.","authors":"Songhui Rao, Miaomiao Liu, Yin Huang, Hongye Yang, Jiarui Liang, Jiayu Lu, Yan Niu, Bin Wang","doi":"10.1088/1741-2552/ad8bf3","DOIUrl":"10.1088/1741-2552/ad8bf3","url":null,"abstract":"<p><p><i>Objective</i>. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation.<i>Approach</i>. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the anchoring temporal convolutional networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms.<i>Main results</i>. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.09 per hour, and an average prediction time (APT) of 98.92 min. Using the CHB-MIT scalp EEG dataset, we achieve 97.44% sensitivity, a FPR of 0.12 per hour, and an APT of 93.54 min.<i>Significance</i>. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The APT of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improve patients' quality of life.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523980","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":"SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.","authors":"Rongkai Liu, Quanjun Song, Tingting Ma, Hongqing Pan, Hao Li, Xinyan Zhao","doi":"10.1088/1741-2552/ad8b6e","DOIUrl":"10.1088/1741-2552/ad8b6e","url":null,"abstract":"<p><p><i>Objective.</i>Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.<i>Approach.</i>We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.<i>Main results.</i>The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.<i>Significance.</i>The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515456","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":"Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment.","authors":"Sandrine Hinrichs, Louise Placidet, Antonin Duret, Colas Authié, Angelo Arleo, Diego Ghezzi","doi":"10.1088/1741-2552/ad8b6f","DOIUrl":"10.1088/1741-2552/ad8b6f","url":null,"abstract":"<p><p><i>Objective</i>. Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.<i>Approach</i>. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.<i>Main results</i>. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.<i>Significance</i>. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515416","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":"High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets.","authors":"Nicholas G Cicero, Nina E Fultz, Hongbae Jeong, Stephanie D Williams, Daniel Gomez, Beverly Setzer, Tracy Warbrick, Manfred Jaschke, Ravij Gupta, Michael Lev, Giorgio Bonmassar, Laura D Lewis","doi":"10.1088/1741-2552/ad8837","DOIUrl":"10.1088/1741-2552/ad8837","url":null,"abstract":"<p><p><i>Objective</i>. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.<i>Approach</i>. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.<i>Main results</i>. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.<i>Significance</i>. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484311","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":"Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.","authors":"Pu Zeng, Liangwei Fan, You Luo, Hui Shen, Dewen Hu","doi":"10.1088/1741-2552/ad8963","DOIUrl":"10.1088/1741-2552/ad8963","url":null,"abstract":"<p><p><i>Objective.</i>The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.<i>Approach.</i>To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.<i>Main results.</i>We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.<i>Significance.</i>This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484380","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":"Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.","authors":"Khanh Ha Nguyen, Yvonne Tran, Ashley Craig, Hung Nguyen, Rifai Chai","doi":"10.1088/1741-2552/ad8b6d","DOIUrl":"10.1088/1741-2552/ad8b6d","url":null,"abstract":"<p><p><i>Objective.</i>While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.<i>Approach.</i>In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.<i>Main results</i>. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.<i>Significance.</i>Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515453","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":"Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.","authors":"Frederic Dehais, Kalou Cabrera Castillos, Simon Ladouce, Pierre Clisson","doi":"10.1088/1741-2552/ad8ef7","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8ef7","url":null,"abstract":"<p><p>Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small, randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction.&#xD;&#xD;Methods: In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes.&#xD;&#xD;Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds.&#xD;&#xD;Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low-density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584029","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":"Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.","authors":"Deniz Kocanaogullari, Richard Gall, Jennifer Mak, Xiaofei Huang, Katie Mullen, Sarah Ostadabbas, George Wittenberg, Emily Stafford Grattan, Murat Akcakaya","doi":"10.1088/1741-2552/ad8efc","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8efc","url":null,"abstract":"<p><strong>Objective: </strong>We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients.</p><p><strong>Approach: </strong>Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.</p><p><strong>Main results: </strong>Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.</p><p><strong>Significance: </strong>These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584031","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":"Safety of non-invasive brain stimulation in patients with implants: a computational risk assessment.","authors":"Fariba Karimi, Antonino M Cassarà, Myles Capstick, Niels Kuster, Esra Neufeld","doi":"10.1088/1741-2552/ad8efa","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8efa","url":null,"abstract":"<p><strong>Objective: </strong>Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications.</p><p><strong>Approach: </strong>We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated.</p><p><strong>Results: </strong>At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded.</p><p><strong>Significance: </strong>Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5).</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584010","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}