Journal of neural engineering最新文献

{"title":"Contactless measurement of muscle fiber conduction velocity - a novel approach using optically pumped magnetometers.","authors":"Lukas Baier, Tim Brümmer, Burak Senay, Markus Siegel, Ahmet Doğukan Keleş, Oliver Röhrle, Thomas Klotz, Nima Noury, Justus Marquetand","doi":"10.1088/1741-2552/adc83b","DOIUrl":"https://doi.org/10.1088/1741-2552/adc83b","url":null,"abstract":"<p><p>Muscle fiber conduction velocity (MFCV) describes the speed at which electrical activity propagates along muscle fibers and is typically assessed using invasive or surface electromyography (EMG). Because electrical currents generate magnetic fields, their propagation velocity can also be measured biomagnetically using magnetomyography (MMG), offering the advantage of a contactless approach. To test this hypothesis, we recorded MMG signals from the right biceps brachii muscle of 24 healthy subjects (12 male, 12 female) using a linear array of seven optically pumped magnetometers (OPMs). Subjects maintained their force for 30 seconds at 20%, 40%, and 60% of their maximum voluntary contraction (MVC). In 20 subjects, propagation of MMG signals was observable, enabling us to localize the innervation zone. We then estimated the MFCV for each condition by cross-correlating double-differentiated MMG signals. To validate our results, we examined whether our MFCV estimations increased with higher force levels, a well-documented characteristic of the neuromuscular system. The median MFCV increased with force significantly (p = 0.007), with median values of 3.2 m/s at 20%, 3.8 m/s at 40%, and 4.4 m/s at 60% across all 20 subjects. Given the exploratory and pioneering nature of measuring magnetic MFCV in MMG using OPMs for the first time, we have demonstrated not only that MFCV can be measured without contact but also that the localization of the innervation zone is possible. This study paves the way for further application and development of quantum sensors for contactless clinical neurophysiology.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775106","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":"Toward the enhancement of affective brain-computer interfaces using dependence within EEG series.","authors":"Yu Pei, Shaokai Zhao, Liang Xie, Bowen Ji, Zhiguo Luo, Chuang Ma, Kun Gao, Xiaomin Wang, Tingyu Sheng, Ye Yan, Erwei Yin","doi":"10.1088/1741-2552/adbfc0","DOIUrl":"10.1088/1741-2552/adbfc0","url":null,"abstract":"<p><p>In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances.<i>Objective</i>. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs.<i>Approach</i>. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test.<i>Main results</i>. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier's outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy.<i>Significance</i>. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618029","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":"Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.","authors":"Boris Revechkis, Tyson Ns Aflalo, Nader Pouratian, Emily Rosario, Debra S Ouellette, Carey Zhang, Kelsie Pejsa","doi":"10.1088/1741-2552/adc3ca","DOIUrl":"10.1088/1741-2552/adc3ca","url":null,"abstract":"<p><p><i>Objective</i>. Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex (PPC) to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex.<i>Approach</i>. A quadriplegic human subject was implanted with multi-electrode recording arrays in the PPC. Neural spiking and local field potentials were recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person movement generation task in addition to closed loop control performance.<i>Main results</i>. We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and 'attached' 3D arm relative to either a visually similar but 'detached' 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors.<i>Significance</i>. In human parietal cortex, single neuron activity and local field potentials responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, this tuning did not affect closed loop brain control in a virtual reality environment when preceded by a text-based decoder training paradigm.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675027","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":"Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.","authors":"Kriti Kacker, Nikole Chetty, Ariel K Feldman, James Bennett, Peter E Yoo, Adam Fry, David Lacomis, Noam Y Harel, Raul G Nogueira, Shahram Majidi, Nicholas L Opie, Jennifer L Collinger, Thomas J Oxley, David F Putrino, Douglas J Weber","doi":"10.1088/1741-2552/adbd78","DOIUrl":"10.1088/1741-2552/adbd78","url":null,"abstract":"<p><p><i>Objective.</i>This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.<i>Approach.</i>vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.<i>Main results.</i>Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.<i>Significance.</i>By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575002","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":"Frequency dependence of cortical somatosensory evoked response to peripheral nerve stimulation with controlled afferent excitation.","authors":"Disha Gupta, Jodi Brangaccio, Helia Mojtabavi, Jonathan S Carp, Jonathan R Wolpaw, N Jeremy Hill","doi":"10.1088/1741-2552/adc204","DOIUrl":"10.1088/1741-2552/adc204","url":null,"abstract":"<p><p><i>Objective.</i>H-reflex targeted neuroplasticity (HrTNP) protocols comprise a promising rehabilitation approach to improve motor function after brain or spinal injury. In this operant conditioning protocol, concurrent measurement of cortical responses, such as somatosensory evoked potentials (SEPs), would be useful for examining supraspinal involvement and neuroplasticity mechanisms. To date, this potential has not been exploited. However, the stimulation parameters used in the HrTNP protocol deviate from the classically recommended settings for SEP measurements. Most notably, it demands a much longer pulse width, higher stimulation intensity, and lower frequency than traditional SEP settings. In this paper, we report SEP measurements performed within the HrTNP stimulation parameter constraints, specifically characterizing the effect of stimulation frequency.<i>Approach.</i>SEPs were acquired for tibial nerve stimulation at three stimulation frequencies (0.2, 1, and 2 Hz) in 13 subjects while maintaining the afferent volley by controlling the direct soleus muscle response via the Evoked Potential Operant Conditioning System. The amplitude and latency of the short-latency P40 and mid-latency N70 SEP components were measured at the central scalp region using non-invasive electroencephalography.<i>Main</i><i>results.</i>As frequency rose from 0.2 Hz, P40 amplitude and latency did not change. In contrast, N70 amplitude decreased significantly (39% decrease at 1 Hz, and 57% decrease at 2 Hz), presumably due to gating effects. N70 latency was not affected. Across all three frequencies, N70 amplitude increased significantly with stimulation intensity and correlated with M-wave amplitude.<i>Significance</i>. We assess SEPs within an HrTNP protocol, focusing on P40 and N70, elicited with controlled afferent excitation at three stimulation frequencies. HrTNP conditioning protocols show promise for enhancing motor function after brain and spinal injuries. While SEPs offer valuable insights into supraspinal involvement, the stimulation parameters in HrTNP often differ from standard SEP measurement protocols. We address these deviations and provide recommendations for effectively integrating SEP assessments into HrTNP studies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143659464","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":"Epileptic seizure detection using heart rate variability from ambulatory ECG: a pseudoprospective study.","authors":"Jieying Li, Ewan S Nurse, David B Grayden, Mark J Cook, Philippa J Karoly","doi":"10.1088/1741-2552/adc33d","DOIUrl":"10.1088/1741-2552/adc33d","url":null,"abstract":"<p><p><i>Objective.</i>Seizure detection algorithms enable clinicians to accurately assess seizure burden for epilepsy diagnosis and long-term management. State-of-the-art algorithms rely on electroencephalography (EEG) data to identify electrographic seizures. Previous research that used non-EEG signals, such as electrocardiography (ECG) and wristband data, were collected in epilepsy monitoring units. We aimed to investigate the feasibility of ECG seizure detection in ambulatory settings.<i>Approach.</i>We developed a patient-independent, machine learning-based seizure detector using ambulatory long-term ECG monitoring data. The model was trained on long-term studies of 47 patients and evaluated pseudoprospectively using event detection on a hold-out test set of 18 patients.<i>Main results.</i>In the hold-out test set, the seizure detector performed better than chance for 14 out of 18 patients. The average sensitivity was 72% and the average specificity was 68% for the whole test cohort. Overall, across training and test sets, the performance was better for patients diagnosed with focal epilepsy and for patients who were identified as responders (had substantial heart rate changes during seizures).<i>Significance.</i>Key contributions of this study include the development of a patient-independent seizure detector using ambulatory data and the introduction of a pseudoprospective evaluation framework, which can benefit chronic ambulatory seizure monitoring.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672138","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":"Enhanced detection of envelope-following responses for objective fitting of cochlear-implant users.","authors":"Julian Matthias Schott, Robin Gransier, Marc Moonen, Jan Wouters","doi":"10.1088/1741-2552/adc6be","DOIUrl":"https://doi.org/10.1088/1741-2552/adc6be","url":null,"abstract":"<p><strong>Objective: </strong>Electrically evoked auditory steady-state responses (EASSRs) are potential neural responses for objectively determining stimulation parameters of cochlear implants (CIs). Unfortunately, they are difficult to detect in electroencephalography (EEG) recordings due to the electrical stimulation artifacts of the CI. This study investigates a novel stimulation paradigm hypothesized to improve artifact removal efficacy via system identification (SI), and therefore to improve response detection and clinical applicability.</p><p><strong>Approach: </strong>An amplitude-modulated (AM) CI stimulation pulse train with a step-wise increase in modulation frequency is created (referred to as SWEEP stimulation). Another stimulation is created by randomly shuffling modulation frequencies of the SWEEP stimulation (referred to as Shuffled- SWEEP stimulation). AM pulse trains with fixed modulation frequency (referred to as conventional AM stimulation), which elicit EASSRs, are also created for comparison. EEG data is collected from four CI users. A supra-threshold stimulation condition is used to investigate whether the SWEEP and Shuffled- SWEEP stimulation can elicit envelope-following responses (EFRs). A sub- threshold stimulation condition allows the collection of artifact-only EEG data, which is used to compare the SI accuracy on recordings from the SWEEP and the conventional AM stimulation.</p><p><strong>Main results: </strong>In all CI users, neural responses, following the SWEEP, Shuffled-SWEEP, and conventional AM stimulation are detected after artifact removal with SI. The validation with artifact-only EEG data shows higher F1 scores when comparing recordings with SWEEP stimulation (F1 = 0.9) to recordings with conventional AM stimulation (F1 = 0.82).</p><p><strong>Significance: </strong>Being able to accurately identify the response within one EEG recording enables the development of effective, online, objective fitting protocols. The increased neural response detection sensitivity with SWEEP stimulation reduces clinical recording time on average by a factor of 2.07. Detecting EFRs following complex stimulation paradigms offers a potential advancement in the systematic assessment of the temporal envelope processing in CI users.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744664","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":"Vestibular implant stimulation: pulse amplitude modulation versus combined pulse rate and amplitude modulation.","authors":"Stan C J van Boxel, Bernd L Vermorken, Benjamin Volpe, Nils Guinand, Angélica Perez-Fornos, Elke M J Devocht, Raymond van de Berg","doi":"10.1088/1741-2552/adc33a","DOIUrl":"10.1088/1741-2552/adc33a","url":null,"abstract":"<p><p><i>Objective</i>. The vestibular implant is a potential treatment approach for bilateral vestibulopathy patients. To restore gaze stabilization, the implant should elicit vestibulo-ocular reflexes (VORs) over a wide range of eye velocities. Different stimulation strategies to achieve this goal were previously described. Vestibular information can be encoded by modulating stimulation amplitude, rate, or a combination of both. In this study, combined rate and amplitude modulation was compared with amplitude modulation, to evaluate their potential for vestibular implant stimulation.<i>Approach</i>. Nine subjects with a vestibulo-cochlear implant participated in this study. Three stimulation strategies were tested. The combined rate and amplitude modulation setting (baseline rate 50%) was compared with amplitude modulation (baseline rate 50%, and baseline rate equal to the maximum rate). The resulting VOR was evaluated.<i>Main results</i>. Combining rate and amplitude modulation, or using amplitude modulation with a baseline equal to the maximum rate, both significantly increased peak eye velocities (PEVs). Misalignment increased with higher PEVs and higher pulse rate. No significant differences were found in PEVs and misalignment, between both stimulation strategies. Amplitude modulation with a baseline rate at 50%, demonstrated the lowest PEVs.<i>Significance</i>. Combining rate and amplitude modulation, or amplitude modulation with a baseline equal to the maximum rate, can both be considered for future vestibular implant fitting.ClinicalTrials.gov Identifier: NCT04918745.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672144","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 NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering<sup />.","authors":"David E Carlson, Ricardo Chavarriaga, Yiling Liu, Fabien Lotte, Bao-Liang Lu","doi":"10.1088/1741-2552/adbfbd","DOIUrl":"10.1088/1741-2552/adbfbd","url":null,"abstract":"<p><p><i>Objective.</i>Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering.<i>Approach.</i>We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering.<i>Main results.</i>Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions.<i>Significance.</i>By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11948487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618027","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":"Classification of schizophrenia based on RAnet-ET: Resnet based attention network for eye-tracking.","authors":"Ruochen Dang, Ying Wang, Feiyu Zhu, Xiaoyi Wang, Jingping Zhao, Ping Shao, Bing Lang, Yuqi Wang, Zhibin Pan, BingLiang Hu, Renrong Wu, Quan Wang","doi":"10.1088/1741-2552/adc5a5","DOIUrl":"https://doi.org/10.1088/1741-2552/adc5a5","url":null,"abstract":"<p><strong>Objective: </strong>There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classification model based on eye-tracking data to assist physicians in the intelligent auxiliary diagnosis of schizophrenia.</p><p><strong>Approach: </strong>This study employed three eye-tracking experiments-Picture-Free Viewing, Smooth Pursuit Tracking, and Fixation Stability-to collect eye-tracking data from patients with schizophrenia and healthy controls. The eye-tracking data of 292 participants (133 healthy controls and 159 patients with schizophrenia) were recorded. Utilizing eye-tracking data in picture-free viewing, we introduce a Resnet-based Attention Network for Eye-Tracking (RAnet-ET) integrated with the attention mechanism. RAnet-ET was trained by employing multiple loss functions to classify patients with schizophrenia and healthy controls. Furthermore, we proposed a classifier for handling multimodal features that combines specific features extracted from the well-trained RAnet-ET, 100 eye-tracking variables extracted from three eye-tracking experiments, and 19 MATRICS Consensus Cognitive Battery scores.</p><p><strong>Main results: </strong>The RAnet-ET achieved good performance in classifying schizophrenia, yielding an accuracy of 89.04%, a specificity of 90.56%, and an F1 score of 87.87%. The classification results based on multimodal features demonstrated improved performance, achieving 96.37% accuracy, 96.87% sensitivity, 95.87% specificity, and 96.37% F1 score.</p><p><strong>Significance: </strong>By integrating attention mechanisms, we designed RAnet-ET, which achieved good performance in classifying schizophrenia from free-viewing eye-tracking data. The synergistic combination of specific features extracted from the well-trained RAnet-ET, MCCB scores, and eye-tracking variables achieved exceptional classification performance, distinguishing individuals with schizophrenia from healthy controls. This study underscores the potential of our approach as a pivotal asset for the diagnosis of schizophrenia.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733820","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}