Journal of neural engineering最新文献

{"title":"Ramped kilohertz-frequency signals produce nerve conduction block without onset response.","authors":"Edgar Peña, Nicole A Pelot, Warren M Grill","doi":"10.1088/1741-2552/add20e","DOIUrl":"10.1088/1741-2552/add20e","url":null,"abstract":"<p><p><i>Objective.</i>Reversible block of peripheral nerve conduction using kilohertz-frequency (KHF) electrical signals has substantial potential for treating diseases. However, onset response, i.e. KHF-induced excitation en route to producing nerve block, is an undesired outcome of neural block protocols. Previous studies of KHF nerve block observed increased onset responses when KHF signal amplitude was linearly ramped for up to 60 s at frequencies up to 30 kHz. Here, we evaluated the onset response across a broad range of ramp durations and frequencies.<i>Approach</i>. In experiments on the rat tibial nerve and biophysical axon models, we quantified nerve responses to linearly ramped KHF signals applied for durations from 16 to 512 s and at frequencies from 10 to 83.3 kHz. We also investigated the role of slow inactivation on onset response during linear ramps by using lacosamide to enhance slow inactivation pharmacologically and by introducing a slow inactivation gating variable in computational models.<i>Main results</i>. In experiments, sufficiently high frequencies (⩾20.8 kHz) with amplitudes that were ramped sufficiently slowly (4.4-570<i>μ</i>A s^-1) generated conduction block without onset response, and increasing frequency enabled shorter ramps to block without onset response. Experimental use of lacosamide to enhance slow inactivation also eliminated onset response. In computational models, the effects of ramp duration/ramp rate on onset response only occurred after introducing a slow inactivation gating variable, and the models did not account for frequency effects.<i>Significance</i>. The results reveal, for the first time, the ability to use charge-balanced linearly ramped KHF signals to block without onset response. This novel approach enhances the precision of neural blocking protocols and enables coordinated neural control to restore organ function, such as in urinary control after spinal cord injury.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028425","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":"Single-microphone deep envelope separation based auditory attention decoding for competing speech and music.","authors":"M Asjid Tanveer, Jesper Jensen, Zheng-Hua Tan, Jan Østergaard","doi":"10.1088/1741-2552/add0e7","DOIUrl":"https://doi.org/10.1088/1741-2552/add0e7","url":null,"abstract":"<p><p><i>Objective.</i>In this study, we introduce an end-to-end single microphone deep learning system for source separation and auditory attention decoding (AAD) in a competing speech and music setup. Deep source separation is applied directly on the envelope of the observed mixed audio signal. The resulting separated envelopes are compared to the envelope obtained from the electroencephalography (EEG) signals via deep stimulus reconstruction, where Pearson correlation is used as a loss function for training and evaluation.<i>Approach.</i>Deep learning models for source envelope separation and AAD are trained on target/distractor pairs from speech and music, covering four cases: speech vs. speech, speech vs. music, music vs. speech, and music vs. music. We convolve 10 different HRTFs with our audio signals to simulate the effects of head, torso and outer ear, and evaluate our model's ability to generalize. The models are trained (and evaluated) on 20 s time windows extracted from 60 s EEG trials.<i>Main results.</i>We achieve a target Pearson correlation and accuracy of 0.122% and 82.4% on the original dataset and an average target Pearson correlation and accuracy of 0.106% and 75.4% across the 10 HRTF variants. For the distractor, we achieve an average Pearson correlation of 0.004. Additionally, our model gives an accuracy of 82.8%, 85.8%, 79.7% and 81.5% across the four aforementioned cases for speech and music. With perfectly separated envelopes, we can achieve an accuracy of 83.0%, which is comparable to the case of source separated envelopes.<i>Significance.</i>We conclude that the deep learning models for source envelope separation and AAD generalize well across the set of speech and music signals and HRTFs tested in this study. We notice that source separation performs worse for a mixed music and speech signal, but the resulting AAD performance is not impacted.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012883","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":"Analysis of electrochemical impedance spectroscopy data for sputtered iridium oxide electrodes.","authors":"Henry M Lutz, Yupeng Wu, Cynthia C Eluagu, Stuart F Cogan, Kevin J Otto, Mark E Orazem","doi":"10.1088/1741-2552/add090","DOIUrl":"https://doi.org/10.1088/1741-2552/add090","url":null,"abstract":"<p><p><i>Objective</i>. Our objective was to perform a complete analysis of<i>in-vitro</i>impedance data for sputtered iridium oxide film (SIROF) micro-electrodes. The analysis included quantification of the stochastic and bias error structure and development of a process model that accounted for the chemistry and physics of the electrode-electrolyte interface.<i>Approach</i>. The measurement model program was used to analyze electrochemical impedance spectroscopy (EIS) data for SIROF micro-electrodes at potentials ranging from -0.4 to +0.6 V(Ag|AgCl). The frequency range used for the analysis was that determined to be consistent with the Kramers-Kronig relations. Interpretation of the data was enabled by truncating frequencies at which the ohmic impedance influenced the impedance.<i>Main results</i>. An interpretation model was developed that considered the impedance of the bare surface and the contribution of a porous component, based on the de Levie model of porous electrodes. The influence of iridium oxidation state on impedance was included. The proposed model fit all 36 EIS spectra well. The effective capacitance of the SIROF system ranged from 32 mF cm^-2at -0.4 V(Ag|AgCl) to a maximum of 93 mF cm^-2at 0.2 and 0.4 V(Ag|AgCl).<i>Significance</i>. The model developed to interpret the impedance response of neural stimulation electrodes<i>in vitro</i>guides model development for<i>in-vivo</i>studies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12056662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144016058","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":"Special issue on brain-computer interfaces: highlighting research from the 10th International Brain-Computer Interface Meeting.","authors":"Jennifer L Collinger, Mariska J Vansteensel, Natalie Mrachacz-Kersting, Donatella Mattia, Davide Valeriani, Theresa M Vaughan","doi":"10.1088/1741-2552/adcaed","DOIUrl":"https://doi.org/10.1088/1741-2552/adcaed","url":null,"abstract":"","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061340","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":"Simplified control of neuromuscular stimulation systems for restoration of reach with limb stiffness as a modifiable degree of freedom.","authors":"Tyler R Johnson, Chase A Haddix, A Bolu Ajiboye, Dawn M Taylor","doi":"10.1088/1741-2552/adc9e3","DOIUrl":"10.1088/1741-2552/adc9e3","url":null,"abstract":"<p><p><i>Objective.</i>Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: (1) optimizing stimulation across muscles with overlap in function, (2) coordinating stimulation across joints, and (3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated.<i>Approach.</i>We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected<i>in silico,</i>and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time.<i>Main results.</i>By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g. 20° angular intervals) still produced good results with median endpoint position errors of less than 1 cm-a range that should be easy to correct for with real-time visual feedback.<i>Significance.</i>Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805251","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":"Spatially precise activation of the mouse cochlea with a multi-channel hybrid cochlear implant.","authors":"Ajmal A Azees, Alex C Thompson, Patrick Ruther, Elise A Ajay, Jenny Zhou, Ulises A Aregueta Robles, David J Garrett, Anita Quigley, James B Fallon, Rachael T Richardson","doi":"10.1088/1741-2552/add091","DOIUrl":"https://doi.org/10.1088/1741-2552/add091","url":null,"abstract":"<p><p><i>Objective.</i>Cochlear implants are among the few clinical interventions for people with severe or profound hearing loss. However, current spread during monopolar electrical stimulation results in poor spectral resolution, prompting the exploration of optical stimulation as an alternative approach. Enabled by introducing light-sensitive ion channels into auditory neurons (optogenetics), optical stimulation has been shown to activate a more discrete neural area with minimal overlap between each frequency channel during simultaneous stimulation. However, the utility of optogenetic approaches is uncertain due to the low fidelity of responses to light and high-power requirements compared to electrical stimulation.<i>Approach.</i>Hybrid stimulation, combining sub-threshold electrical and optical pulses, has been shown to improve fidelity and use less light, but the impact on spread of activation and channel summation using a translatable, multi-channel hybrid implant is unknown. This study examined these factors during single channel and simultaneous multi-channel hybrid stimulation in transgenic mice expressing the ChR2/H134R opsin. Acutely deafened mice were implanted with a hybrid cochlear array containing alternating light emitting diodes and platinum electrode rings. Spiking activity in the inferior colliculus was recorded during electrical-only or hybrid stimulation in which optical and electrical stimuli were both at sub-threshold intensities. Thresholds, spread of activation, and threshold shifts during simultaneous hybrid stimulation were compared to electrical-only stimulation.<i>Main results.</i>The electrical current required to reach activation threshold during hybrid stimulation was reduced by 7.3 dB compared to electrical-only stimulation (<i>p</i>< 0.001). The activation width measured at two levels of discrimination above threshold and channel summation during simultaneous hybrid stimulation were significantly lower compared to electrical-only stimulation (<i>p</i>< 0.05), but there was no spatial advantage of hybrid stimulation at higher electrical stimulation levels.<i>Significance.</i>Reduced channel interaction would facilitate multi-channel simultaneous stimulation, thereby enhancing the perception of temporal fine structure which is crucial for music and speech in noise.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065520","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":"Universal semantic feature extraction from EEG signals: a task-independent framework.","authors":"Hossein Ahmadi, Luca Mesin","doi":"10.1088/1741-2552/add08f","DOIUrl":"https://doi.org/10.1088/1741-2552/add08f","url":null,"abstract":"<p><p><i>Objective.</i>Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations.<i>Approach.</i>We propose a novel framework integrating convolutional neural networks, AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials (SSVEPs), and event-related potentials (ERPs, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features.<i>Main results.</i>Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV_2a and BCICIV_2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-distributed stochastic neighbor embedding and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the selected model configuration.<i>Significance.</i>This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface applications, cross-task EEG analysis, and future developments in semantic EEG processing.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036027","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":"Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural dynamics to precision anesthesia.","authors":"Xing Jin, Zhenhu Liang, Fu Li, Xiaoli Li","doi":"10.1088/1741-2552/add0e6","DOIUrl":"https://doi.org/10.1088/1741-2552/add0e6","url":null,"abstract":"<p><p><i>Objective.</i>Understanding the neural mechanisms underlying consciousness during anesthesia is critical for advancing anesthesiology and neuroscience. However, given the high variability in individual sensitivity to anesthetic agents, accurately elucidating the relationship between individual characteristics and drug responses is also crucial for ensuring clinical anesthesia safety.<i>Approach.</i>This study utilized high-density EEG data from 20 participants under various propofol-induced sedation states. We stratified participants into low- and high-sensitivity cohorts based on their behavioral responsiveness to standardized auditory stimuli during sedation. Then the metrics such as permutation entropy (PE), phase-lag entropy (PLE), and permutation cross mutual information (PCMI) were analyzed to evaluate neural complexity, the diversity of connectivity, and information integration. Machine learning models, including support vector machines (SVM), were applied to classify individual sensitivity to propofol, with SHapley Additive exPlanations (SHAP) analysis providing feature interpretability.<i>Main results.</i>Subjects were divided into high-performance (low-sensitivity) group and low-performance (high-sensitivity) group based on the accuracy of their responses to auditory stimuli. In the moderate sedation, the high-performance group exhibited elevated PE, increased PLE in alpha band and the decreased PLE in beta band, and decreased PCMI in alpha band. In the resting-state, we extracted 18 metrics that were significantly different between the two groups. Using these resting-state metrics as features, the SVM model achieved an accuracy of 87.5% ± 0.06% in classifying individuals into high- or low-sensitivity groups. SHAP analysis results indicated that the features, including the PLE value of temporal in alpha band (<i>α</i>-PLET) and the PCMI value of frontal-parietal in beta band (<i>β</i>-PCMIFP), were identified as robust predictors of propofol sensitivity, with high weights across various models.<i>Significance.</i>This study highlights the differential neural dynamics induced by propofol across performance groups. This study highlights that resting-state metrics can predict individual sensitivity to propofol. Our findings provide preliminary insights into the potential utility of pre-anesthesia brain state assessments in predicting individual propofol sensitivity, which may contribute to the development of more precise personalized anesthesia plans.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029854","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 depression detection approach leveraging transfer learning with single-channel EEG.","authors":"Chengyuan Sun, Mingjuan Guan, Keyu Duan, Shang Gao, Zhao Chen","doi":"10.1088/1741-2552/adcfc8","DOIUrl":"https://doi.org/10.1088/1741-2552/adcfc8","url":null,"abstract":"<p><p><i>Objective.</i>Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy individuals. However, most current methods detect depression based on multichannel EEG signals, which constrains its application in daily life. The context in which EEG is obtained can vary in terms of study designs and EEG equipment settings, and the available depression EEG data is limited, which could also potentially lessen the efficacy of the model in differentiating between MDD and healthy subjects. To solve the above challenges, a depression detection model leveraging transfer learning with the single-channel EEG is advanced.<i>Approach.</i>We utilized a pretrained ResNet152V2 network to which a flattening layer and dense layer were appended. The method of feature extraction was applied, meaning that all layers within ResNet152V2 were frozen and only the parameters of the newly added layers were adjustable during training. Given the superiority of deep neural networks in image processing, the temporal sequences of EEG signals are first converted into images, transforming the problem of EEG signal categorization into an image classification task. Subsequently, a cross-subject experimental strategy was adopted for model training and performance evaluation.<i>Main results.</i>The model was capable of precisely (approaching 100% accuracy) identifying depression in other individuals by employing single-channel EEG samples obtained from a limited number of subjects. Furthermore, the model exhibited superior performance across four publicly available depression EEG datasets, thereby demonstrating good adaptability in response to variations in EEG caused by the context.<i>Significance.</i>This research not only highlights the impressive potential of deep transfer learning techniques in EEG signal analysis but also paves the way for innovative technical approaches to facilitate early diagnosis of associated mental disorders in the future.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045421","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":"FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.","authors":"Wenqian Feng, Yanna Zhao, Hao Peng, Chenxi Nie, Hongbin Lv, Shuai Wang, Hailing Feng","doi":"10.1088/1741-2552/adce33","DOIUrl":"https://doi.org/10.1088/1741-2552/adce33","url":null,"abstract":"<p><p><i>Objective</i>. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in feature extraction. This study aims to develop a hybrid model that integrates the advantages of convolutional neural networks (CNNs) and Transformer to enhance seizure prediction performance.<i>Approach</i>. We propose FusionXNet, a hybrid model inspired by CNNs and Transformer architectures, for seizure prediction. Specifically, we design a token synthesis unit to extract local features using convolution operations and capture global electroencephalography (EEG) representations via attention mechanisms. By merging local and global features extracted from the EEG segments, FusionXNet enhances feature representations, which are subsequently fed into a classifier for final seizure prediction.<i>Main results</i>. We evaluate the model on the publicly available Boston Children's Hospital and the Massachusetts Institute of Technology dataset, conducting segment-based and event-based experiments in both patient-specific and cross-patient settings. In event-based patient-specific experiments, FusionXNet achieves a sensitivity of 97.602% and a false positive rate (FPR) of 0.059 h^-1. The results demonstrate that the proposed model effectively predicts seizures with high sensitivity and a low FPR, outperforming existing methods.<i>Significance</i>. The proposed FusionXNet model provides a robust and efficient approach for seizure prediction by leveraging both local and global feature extraction. The high sensitivity and low FPR indicate its potential for real-world clinical applications, improving patient management and reducing healthcare costs.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036094","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}