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ORCIDBrain Informatics, Volume 12
Volume 12, Number 1, December 2025
- Venkatesh Upadrista, Sajid Nazir, Huaglory Tianfield:
Blockchain-enabled digital twin system for brain stroke prediction. 1 - Xiaofu He, Yian Wang, Yutong Gao, Xuchen Wang, Zhixiong Sun, Huixiang Zhu, Kam W. Leong, Bin Xu:
CalciumZero: a toolbox for fluorescence calcium imaging on iPSC derived brain organoids. 2 - Colin Birkenbihl
, Madison Cuppels, Rory T. Boyle, Hannah M. Klinger, Oliver Langford, Gillian T. Coughlan, Michael J. Properzi, Jasmeer P. Chhatwal, Julie C. Price, Aaron P. Schultz, Dorene M. Rentz, Rebecca E. Amariglio, Keith A. Johnson, Rebecca F. Gottesman, Shubhabrata Mukherjee, Paul Maruff, Yen Ying Lim, Colin L. Masters, Alexa Beiser, Susan M. Resnick, Timothy M. Hughes, Samantha Burnham, Ilke Tunali, Susan M. Landau, Ann D. Cohen, Sterling C. Johnson, Tobey J. Betthauser, Sudha Seshadri, Samuel N. Lockhart, Sid E. O'Bryant, Prashanthi Vemuri, Reisa A. Sperling, Timothy J. Hohman, Michael C. Donohue, Rachel F. Buckley:
Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease. 3 - Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen:
Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline. 4 - Gauttam Jangir, Nisheeth Joshi, Gaurav Purohit:
Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach. 5 - Xiaojia Wang, Dayang Wu, Chunfeng Yang:
Localization of epileptic foci from intracranial EEG using the GRU-GC algorithm. 6 - Yan Xian, Hong Yu, Ye Wang, Guoyin Wang:
Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing. 7 - Maryam Akhavan Aghdam, Serdar Bozdag, Fahad Saeed:
Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation. 8 - René Lehmann, Bodo Vogt:
Breakdown of the compositional data approach in psychometric Likert scale big data analysis: about the loss of statistical power of two-sample t-tests applied to heavy-tailed big data. 9 - Shagufta Iftikhar, Nadeem Anjum, Abdul Basit Siddiqui, Masood Ur Rehman, Naeem Ramzan:
Explainable CNN for brain tumor detection and classification through XAI based key features identification. 10 - Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem:
HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers. 11 - Matthew Littman, Huy-Binh Nguyen, Joanna Campbell, Katelyn R. Keyloun:
Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder. 12 - Md. Nurul Ahad Tawhid, Siuly Siuly, Md. Enamul Kabir, Yan Li:
Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis. 13 - Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Feng Zhao, Jianming Yong:
AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts. 14 - Paolo Sorino, Angela Lombardi, Domenico Lofù, Tommaso Colafiglio, Antonio Ferrara, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia:
Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence. 15 - Vitaly I. Dobromyslin, Wenjin Zhou:
Enhancing cerebral infarct classification by automatically extracting relevant fMRI features. 16
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