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Brain Informatics, Volume 11
Volume 11, Number 1, December 2024
- Changhong Jing, Hongzhi Kuai, Hiroki Matsumoto, Tomoharu Yamaguchi, Iman Yi Liao, Shuqiang Wang:
Addiction-related brain networks identification via Graph Diffusion Reconstruction Network. 1 - Sara Saponaro, Francesca Lizzi, Giacomo Serra, Francesca Mainas, Piernicola Oliva, Alessia Giuliano, Sara Calderoni, Alessandra Retico:
Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders. 2 - Muhammad Atta Othman Ahmed, Yasser AbdelSatar, Eed M. Darwish, Elnomery Allam Zanaty:
Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics. 3 - Ilaria Gigi, Rosa Senatore, Angelo Marcelli:
The onset of motor learning impairments in Parkinson's disease: a computational investigation. 4 - Kaida Ning, Pascale B. Cannon, Jiawei Yu, Srinesh Shenoi, Lu Wang, Joydeep Sarkar:
3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer's disease sub-scores. 5 - Wei Pei, Yan Li, Peng Wen, Fuwen Yang, Xiaopeng Ji:
An automatic method using MFCC features for sleep stage classification. 6 - Pragati Patel, Sivarenjani Balasubramanian, Ramesh Naidu Annavarapu:
Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier. 7 - Shihao Yang, Meng Jiao, Jing Xiang, Neel Fotedar, Hai Sun, Feng Liu:
Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation. 8 - Jamie L. Hanson, Dorthea J. Adkins, Eva Bacas, Peiran Zhou:
Examining the reliability of brain age algorithms under varying degrees of participant motion. 9 - Viswan Vimbi, Noushath Shaffi, Mufti Mahmud:
Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection. 10 - Andrés Felipe Soler, Eduardo Giraldo, Marta Molinas:
EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels. 11 - Diah Risqiwati, Adhi Dharma Wibawa, Evi Septiana Pane, Eko Mulyanto Yuniarno, Wardah Rahmatul Islamiyah, Mauridhi Hery Purnomo:
Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation. 12 - Sharan J. Prakash, Kimberly M. Van Auken, David P. Hill, Paul W. Sternberg:
Correction: Semantic representation of neural circuit knowledge in Caenorhabditis elegans. 13 - Navneet Agarwal, Gaël Dias, Sonia Dollfus:
Multi-view graph-based interview representation to improve depression level estimation. 14 - Yoon Kyoung Choi, Linqing Feng, Won-Ki Jeong, Jinhyun Kim:
Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity. 15 - Atefe Aghaei, Mohsen Ebrahimi Moghaddam:
Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection. 16 - Md. Fazlul Karim Khondakar, Md. Hasib Sarowar, Mehdi Hasan Chowdhury, Sumit Majumder, Md. Azad Hossain, M. Ali Akber Dewan, Quazi Delwar Hossain:
A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. 17 - Keerthi S. Chandran, Kuntal Ghosh:
A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus. 18 - Rene Lehmann, Bodo Vogt:
Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach. 19 - J. Farineau, Rémy Lestienne:
Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator. 20 - Rajdeep Bhadra, Pawan Kumar Singh, Mufti Mahmud:
HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals. 21 - Xiaojia Wang, Yanchao Liu, Chunfeng Yang:
Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy. 22
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