Neuroscience informatics最新文献

Enhancing seizure detection with hybrid XGBoost and recurrent neural networks 混合XGBoost和循环神经网络增强癫痫检测

Neuroscience informatics Pub Date : 2025-05-05 DOI: 10.1016/j.neuri.2025.100206

Santushti Santosh Betgeri , Madhu Shukla , Dinesh Kumar , Surbhi B. Khan , Muhammad Attique Khan , Nora A. Alkhaldi

{"title":"Enhancing seizure detection with hybrid XGBoost and recurrent neural networks","authors":"Santushti Santosh Betgeri , Madhu Shukla , Dinesh Kumar , Surbhi B. Khan , Muhammad Attique Khan , Nora A. Alkhaldi","doi":"10.1016/j.neuri.2025.100206","DOIUrl":"10.1016/j.neuri.2025.100206","url":null,"abstract":"<div><div>Epileptic seizures are sudden and unpredictable, posing serious health risks and significantly affecting the quality of life of patients. An accurate and timely prediction system can help mitigate these risks by enabling preventive measures and improving patient safety. This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. Signal processing techniques were applied to enhance data quality, and all models were trained on the same dataset for binary classification. Sixteen models were evaluated, including traditional classifiers such as Logistic Regression, K-Nearest Neighbors, Decision Trees, ensemble methods that include Random Forest, Gradient Boosting, and advanced techniques such as Extreme Gradient Boosting, Support Vector Machines, Gated Recurrent Units, and Long Short-Term Memory networks. Performance was assessed using multiple evaluation metrics on both training and validation datasets. While simpler models showed varied accuracy, ensemble and deep learning models performed significantly better, with hybrid approaches demonstrating strong generalization. Results show that whereas ensemble and deep learning models far exceeded simpler models, their accuracy varied. AUC of 0.995 and accuracy of 98.2% on validation data and 0.994 AUC with 96.8% accuracy on test data were obtained by the proposed hybrid Model integrating XGBoost with RNN-based architectures (LSTM and GRU). High recall (96.2%) shown by the Model guarantees minimal false negatives and is important for clinical uses. Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916826","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}

引用次数: 0

A MATLAB-based tool for converting fNIRS time-series data to Homer3-compatible formats 一个基于matlab的工具，用于将fNIRS时间序列数据转换为homer3兼容格式

Neuroscience informatics Pub Date : 2025-05-05 DOI: 10.1016/j.neuri.2025.100205

Chao Wang , Xiaojun Cheng , Shichao Liu

引用次数: 0

Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals 基于深度学习的多脑胶囊网络在新一代临床情绪识别中的应用

Neuroscience informatics Pub Date : 2025-04-28 DOI: 10.1016/j.neuri.2025.100203

Ritu Dahiya , Mamatha G , Shila Sumol Jawale , Santanu Das , Sagar Choudhary , Vinod Motiram Rathod , Bhawna Janghel Rajput

{"title":"Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals","authors":"Ritu Dahiya , Mamatha G , Shila Sumol Jawale , Santanu Das , Sagar Choudhary , Vinod Motiram Rathod , Bhawna Janghel Rajput","doi":"10.1016/j.neuri.2025.100203","DOIUrl":"10.1016/j.neuri.2025.100203","url":null,"abstract":"<div><div>Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916824","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}

引用次数: 0

AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory 人工智能驱动的EEG神经科学分析评估情绪对错误记忆的影响

Neuroscience informatics Pub Date : 2025-04-15 DOI: 10.1016/j.neuri.2025.100201

V. Mahalakshmi

{"title":"AI-driven EEG neuroscientific analysis for evaluating the influence of emotions on false memory","authors":"V. Mahalakshmi","doi":"10.1016/j.neuri.2025.100201","DOIUrl":"10.1016/j.neuri.2025.100201","url":null,"abstract":"<div><div>Investigating the brain mechanisms behind memory processing depends on an awareness of how emotions influence false memory. This study used AI-driven EEG microstate analysis to investigate how emotions affect the generation of false memories from both a temporal and a geographic perspective. Within emotional groups, AI-augmented computational models showed distinct brain processing patterns, particularly during the recall processing stage. By altering cognitive processing dynamics, these results support the hypothesis that AI-enhanced brain activity analysis can effectively mimic the influence of emotional states on the formation of false memories. This work explores emotional implications on false memory by combining artificial intelligence (AI) with EEG-based microstate analysis, therefore offering greater understanding of brain dynamics at several cognitive phases. EEG data collected under various emotional states were analyzed using AI-powered techniques to enable exact extraction of microstate templates (Microstates 1–5) for every emotional group. Phase-locked value (AI-PLV) brain functional networks were built inside microstates displaying notable temporal coverage variations. Driven by artificial intelligence, temporal and geographical analysis of EEG signals revealed different brain processing mechanisms among emotional groupings. The group with pleasant emotions showed continuous activity in prefrontal Microstates 3 and 5, therefore suggesting improved cognitive processing. Reflecting a concentration on information integration, the neutral group showed extended involvement in central-active Microstates 3 and 4. These results emphasize how artificial intelligence is helping neuroscientific research to progress by offering a strong framework for comprehending AI-driven emotional-based aberrations in memory recall.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843617","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}

引用次数: 0

Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study 通过综合放射学特征增强未破裂脑动静脉畸形头痛表现的检测：一项横断面研究

Neuroscience informatics Pub Date : 2025-04-04 DOI: 10.1016/j.neuri.2025.100200

Chia-Yu Liu , Chia-Feng Lu , Jr-Wei Wu , Yong-Sin Hu , Jih-Yuan Lin , Huai-Che Yang , Jing-Kai Loo , Feng-Chi Chang , Kang-Du Liu , Chung-Jung Lin

{"title":"Enhanced detection of headache presentation in unruptured brain arteriovenous malformation through combined radiologic features: A cross-sectional study","authors":"Chia-Yu Liu , Chia-Feng Lu , Jr-Wei Wu , Yong-Sin Hu , Jih-Yuan Lin , Huai-Che Yang , Jing-Kai Loo , Feng-Chi Chang , Kang-Du Liu , Chung-Jung Lin","doi":"10.1016/j.neuri.2025.100200","DOIUrl":"10.1016/j.neuri.2025.100200","url":null,"abstract":"<div><h3>Background</h3><div>Although determining angioarchitecture provide qualitative insights into headache-susceptible brain arteriovenous malformation (BAVM), the potential of quantitative radiomics to detect headache in unruptured BAVM remains unclear. We developed classification models that integrate radiomic features and angioarchitecture to assist unruptured BAVM headache treatment decision-making.</div></div><div><h3>Methods</h3><div>We considered patients with unruptured BAVM who underwent magnetic resonance imaging between 2010 and 2023. 146 radiomic features were assessed. Radiomic features were delineated, and angioarchitecture was analyzed. Statistical analyses, including least absolute shrinkage and selection operator regression and logistic regression, were used to select features and develop models. Receiver operating characteristic and decision curve analyses were performed to evaluate performance.</div></div><div><h3>Results</h3><div>The clinical model based on age, sex, and parieto-occipital lesion location achieved an area under the curve (AUC) of 0.741. Adding two significant radiomic features and one angioarchitecture feature enhanced the models. The radiomic and angioarchitecture models achieved an AUC of 0.763. The combined model, with an AUC of 0.799, significantly outperformed the clinical model (<span><math><mi>P</mi><mo>=</mo><mn>0.046</mn></math></span>). Decision curve analysis indicated that the combined model performed best at threshold probabilities between 15% and 40%.</div></div><div><h3>Conclusion</h3><div>Integrating radiomic features and angioarchitecture enhances the identification of unruptured BAVM headache.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807506","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}

引用次数: 0

Leveraging transparent ontology learning to refine constructs in neuroscience 利用透明的本体学习来完善神经科学的结构

Neuroscience informatics Pub Date : 2025-03-28 DOI: 10.1016/j.neuri.2025.100199

David Moreau, Kristina Wiebels

引用次数: 0

Feature fusion based deep learning model for Alzheimer's neurological disorder classification 基于特征融合的深度学习阿尔茨海默病神经障碍分类模型

Neuroscience informatics Pub Date : 2025-03-19 DOI: 10.1016/j.neuri.2025.100196

Arhath Kumar , S. Pradeep , Kumud Arora , G. Sreeram , A. Pankajam , Trupti Patil , Aradhana Sahu

{"title":"Feature fusion based deep learning model for Alzheimer's neurological disorder classification","authors":"Arhath Kumar , S. Pradeep , Kumud Arora , G. Sreeram , A. Pankajam , Trupti Patil , Aradhana Sahu","doi":"10.1016/j.neuri.2025.100196","DOIUrl":"10.1016/j.neuri.2025.100196","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a severe brain disorder that can cause degradation of brain tissue and memory loss. Owing to Alzheimer's disease's high cost, a number of deep learning-based models have been put out to accurately identify the illness. This study introduces a new way to classify Alzheimer's disease using deep learning and combining different types of features. The 3D lightweight MBANet developed in this research has less parameters and can concentrate on more discriminative deep structures than conventional artificial neural networks like CNN, according to experimental data. We first create a Multi-Branch Attention Network (MBANet) to gather detailed features of the hippocampus from large sets of data. A new method is created to capture texture features in the hippocampus. It uses two techniques: multi-Tree Wavelet Transform (MTWT) and Gray Length Matrix (GLM). This method works in three dimensions and at different scales. Also, standard methods are used to measure the size and shape of the hippocampus. A mixed feature fusion network is created to simplify and combine data from the hippocampus, helping to classify Alzheimer's disease more effectively. Tests on the EADC-ADNI dataset show that the proposed method for classifying Alzheimer's disease achieves an accuracy of 93.39%, a <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 93.10%, and an AUC of 93.21%. The test results show that the proposed method for classifying Alzheimer's disease is effective and better than traditional methods.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696805","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}

引用次数: 0

Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram 基于无创脑刺激的经颅红外心电图睡眠阶段分类

Neuroscience informatics Pub Date : 2025-03-18 DOI: 10.1016/j.neuri.2025.100197

Janjhyam Venkata Naga Ramesh , Aadam Quraishi , Yassine Aoudni , Mustafa Mudhafar , Divya Nimma , Monika Bansal

{"title":"Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram","authors":"Janjhyam Venkata Naga Ramesh , Aadam Quraishi , Yassine Aoudni , Mustafa Mudhafar , Divya Nimma , Monika Bansal","doi":"10.1016/j.neuri.2025.100197","DOIUrl":"10.1016/j.neuri.2025.100197","url":null,"abstract":"<div><div>Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681918","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}

引用次数: 0

Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions 不同脑卒中患者临床记录的运动障碍语料库分析与开发

Neuroscience informatics Pub Date : 2025-03-18 DOI: 10.1016/j.neuri.2025.100198

Oindrila Banerjee , K.V.N. Sita Mahalakshmi , M.V.S. Jyothi , D. Govind , U.K. Rakesh , A. Rajeev , K. Samudravijaya , Akhilesh Kumar Dubey , Suryakanth V. Gangashetty

{"title":"Analysis and development of clinically recorded dysarthric speech corpus for patients affected with various stroke conditions","authors":"Oindrila Banerjee , K.V.N. Sita Mahalakshmi , M.V.S. Jyothi , D. Govind , U.K. Rakesh , A. Rajeev , K. Samudravijaya , Akhilesh Kumar Dubey , Suryakanth V. Gangashetty","doi":"10.1016/j.neuri.2025.100198","DOIUrl":"10.1016/j.neuri.2025.100198","url":null,"abstract":"<div><div>The manuscript presents the work related to the development of a dysarthric speech corpus for various types of stroke conditions. The corpus consists of speech recorded from 50 stroke patients and 50 healthy controls in clinical environments. Severity of stroke for each patient has been assessed by the clinician based on the National Institute of Health Stroke Scale. The text read by patients and healthy controls comprises (a) five sustained vowels, (b) three words consisting of the plosive consonant and vowels, and (c) 10 phonetically rich sentences in Telugu language. A discriminative analysis is carried out using conventional Mel Frequency Cepstral Coefficients and Convolutional Neural Networks to quantify the perceptual variations in dysarthric speech of stroke patients and healthy controls. Vowels and word utterances of the speech corpus exhibited better class discrimination characteristics compared to sentences for text dependent and speaker independent scenarios.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696804","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}

引用次数: 0

Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing 基于软件的认知方法与类脑计算机机器的集成，实现高效的认知计算

Neuroscience informatics Pub Date : 2025-03-13 DOI: 10.1016/j.neuri.2025.100194

Chitrakant Banchhor , Manoj Kumar Rawat , Rahul Joshi , Dharmesh Dhabliya , Omkaresh Kulkarni , Sandeep Dwarkanath Pande , Umesh Pawar

{"title":"Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing","authors":"Chitrakant Banchhor , Manoj Kumar Rawat , Rahul Joshi , Dharmesh Dhabliya , Omkaresh Kulkarni , Sandeep Dwarkanath Pande , Umesh Pawar","doi":"10.1016/j.neuri.2025.100194","DOIUrl":"10.1016/j.neuri.2025.100194","url":null,"abstract":"<div><div>The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643546","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}

引用次数: 0