{"title":"A graph-based neural approach to linear sum assignment problems","authors":"Carlo Aironi, Samuele Cornell, Stefano Squartini","doi":"10.1142/s0129065724500114","DOIUrl":"https://doi.org/10.1142/s0129065724500114","url":null,"abstract":"","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"429 ","pages":""},"PeriodicalIF":8.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks","authors":"Ruibin Zhao, Zhiwei Xie, Yipeng Zhuang, P. L. Yu","doi":"10.1142/s0129065724500096","DOIUrl":"https://doi.org/10.1142/s0129065724500096","url":null,"abstract":"","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"2015 7","pages":""},"PeriodicalIF":8.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Cuciniello, T. Amorese, C. Greco, Zoraida Callejas Carrión, Carl Vogel, G. Cordasco, Anna Esposito
{"title":"Cultural Differences in the Assessment of Synthetic Voices","authors":"M. Cuciniello, T. Amorese, C. Greco, Zoraida Callejas Carrión, Carl Vogel, G. Cordasco, Anna Esposito","doi":"10.1142/s0129065724500084","DOIUrl":"https://doi.org/10.1142/s0129065724500084","url":null,"abstract":"","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"12 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Mercaldo, Marcello Di Giammarco, Fabrizio Ravelli, Fabio Martinelli, A. Santone, M. Cesarelli
{"title":"Alzheimer's Disease Evaluation through Visual Explainability by means of Convolutional Neural Networks","authors":"F. Mercaldo, Marcello Di Giammarco, Fabrizio Ravelli, Fabio Martinelli, A. Santone, M. Cesarelli","doi":"10.1142/s0129065724500072","DOIUrl":"https://doi.org/10.1142/s0129065724500072","url":null,"abstract":"","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 5","pages":""},"PeriodicalIF":8.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybrid Network for Patient-Specific Seizure Prediction from EEG Data.","authors":"Yongfeng Zhang, Tiantian Xiao, Ziwei Wang, Hongbin Lv, Shuai Wang, Hailing Feng, Shanshan Zhao, Yanna Zhao","doi":"10.1142/S0129065723500569","DOIUrl":"10.1142/S0129065723500569","url":null,"abstract":"<p><p>Seizure prediction can improve the quality of life for patients with drug-resistant epilepsy. With the rapid development of deep learning, lots of seizure prediction methods have been proposed. However, seizure prediction based on single convolution models is limited by the inherent defects of convolution itself. Convolution pays attention to the local features while underestimates the global features. The long-term dependence of the electroencephalogram (EEG) data cannot be captured. In view of these defects, a hybrid model called STCNN based on Swin transformer (ST) and 2D convolutional neural network (2DCNN) is proposed. Time-frequency features extracted by short-term Fourier transform (STFT) are taken as the input of STCNN. ST blocks are used in STCNN to capture the global information and long-term dependencies of EEGs. Meanwhile, the 2DCNN blocks are adopted to capture the local information and short-term dependent features. The combination of the two blocks can fully exploit the seizure-related information thus improve the prediction performance. Comprehensive experiments are performed on the CHB-MIT scalp EEG dataset. The average seizure prediction sensitivity, the area under the ROC curve (AUC) and the false positive rate (FPR) are 92.94%, 95.56% and 0.073, respectively.</p>","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2350056"},"PeriodicalIF":8.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46172061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.","authors":"Dongsheng Liu, Xingchen Dong, Dong Bian, Weidong Zhou","doi":"10.1142/S0129065723500545","DOIUrl":"10.1142/S0129065723500545","url":null,"abstract":"<p><p>Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.</p>","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":" ","pages":"2350054"},"PeriodicalIF":8.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10161304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sepehr Shirani, Antonio Valentin, Bahman Abdi-Sargezeh, Gonzalo Alarcon, Saeid Sanei
{"title":"Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer.","authors":"Sepehr Shirani, Antonio Valentin, Bahman Abdi-Sargezeh, Gonzalo Alarcon, Saeid Sanei","doi":"10.1142/S0129065723500508","DOIUrl":"10.1142/S0129065723500508","url":null,"abstract":"<p><p>Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.</p>","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":" ","pages":"2350050"},"PeriodicalIF":8.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9979245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task.","authors":"Han-Lin Wang, Yun-Ting Kuo, Yu-Chun Lo, Chao-Hung Kuo, Bo-Wei Chen, Ching-Fu Wang, Zu-Yu Wu, Chi-En Lee, Shih-Hung Yang, Sheng-Huang Lin, Po-Chuan Chen, You-Yin Chen","doi":"10.1142/S012906572350051X","DOIUrl":"10.1142/S012906572350051X","url":null,"abstract":"<p><p>Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.</p>","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":" ","pages":"2350051"},"PeriodicalIF":8.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10448794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}