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Brain Informatics

16th International Conference, BI 2023, Hoboken, NJ, USA, August 1–3, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Feng Liu

    1. Stevens Institute of Technology, Hoboken, USA

  2. Yu Zhang

    1. Lehigh University, Bethlehem, USA

  3. Hongzhi Kuai

    1. Maebashi Institute of Technology, Maebashi, Japan

  4. Emily P. Stephen

    1. Boston University, Boston, USA

  5. Hongjun Wang

    1. Stevens Institute of Technology, Hoboken, USA

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13974)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: BI 2023.

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About this book

This book constitutes the proceedings of the 16th International Conference on Brain Informatics, BI 2023, which was held in Hoboken, NJ, USA, during August 1–3, 2023.

The 40 full papers presented in this book were carefully reviewed and selected from 101 submissions. The papers are divided into the following topical sections: cognitive and computational foundations of brain science; investigations of human Information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; brain-machine intelligence and brain-inspired computing; and the 5th international workshop on cognitive neuroscience of thinking and reasoning.


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Table of contents (40 papers)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Cognitive and Computational Foundations of Brain Science

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Fusing Structural and Functional Connectivities Using Disentangled VAE for Detecting MCI

      • Qiankun Zuo, Yanfei Zhu, Libin Lu, Zhi Yang, Yuhui Li, Ning Zhang
      Pages 3-13

    3. Modulation of Beta Power as a Function of Attachment Style and Feedback Valence

      • Dor Mizrahi, Ilan Laufer, Inon Zuckerman
      Pages 14-20

    4. Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry

      • Kostas Georgiadis, Fotis P. Kalaganis, Vangelis P. Oikonomou, Spiros Nikolopoulos, Nikos A. Laskaris, Ioannis Kompatsiaris
      Pages 21-32

    5. A Model of the Contribution of Interneuron Diversity to Recurrent Network Oscillation Generation and Information Coding

      • Gabriel Matías Lorenz, Pablo Martínez-Cañada, Stefano Panzeri
      Pages 33-44

    6. Measuring Stimulus-Related Redundant and Synergistic Functional Connectivity with Single Cell Resolution in Auditory Cortex

      • Loren Koçillari, Marco Celotto, Nikolas A. Francis, Shoutik Mukherjee, Behtash Babadi, Patrick O. Kanold et al.
      Pages 45-56

    7. Fusing Simultaneously Acquired EEG and fMRI via Hierarchical Deep Transcoding

      • Xueqing Liu, Paul Sajda
      Pages 57-67

  3. Investigations of Human Information Processing Systems

    1. Front Matter

      Pages 69-69
      Download chapter PDF

    2. Decoding Emotion Dimensions Arousal and Valence Elicited on EEG Responses to Videos and Images: A Comparative Evaluation

      • Luis Alfredo Moctezuma, Kazuki Sato, Marta Molinas, Takashi Abe
      Pages 71-82

    3. Stabilize Sequential Data Representation via Attraction Module

      • Petr Kuderov, Evgenii Dzhivelikian, Aleksandr I. Panov
      Pages 83-95

    4. Investigating the Generative Dynamics of Energy-Based Neural Networks

      • Lorenzo Tausani, Alberto Testolin, Marco Zorzi
      Pages 96-108

    5. Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease

      • Tanjim Mahmud, Koushick Barua, Anik Barua, Sudhakar Das, Nanziba Basnin, Mohammad Shahadat Hossain et al.
      Pages 109-120

  4. Brain Big Data Analytics, Curation and Management

    1. Front Matter

      Pages 121-121
      Download chapter PDF

    2. Effects of EEG Electrode Numbers on Deep Learning-Based Source Imaging

      • Jesse Rong, Rui Sun, Yuxin Guo, Bin He
      Pages 123-132

    3. Graph Diffusion Reconstruction Network for Addictive Brain-Networks Identification

      • Changhong Jing, Changwei Gong, Zuxin Chen, Shuqiang Wang
      Pages 133-145

    4. MR Image Super-Resolution Using Wavelet Diffusion for Predicting Alzheimer’s Disease

      • Guoli Huang, Xuhang Chen, Yanyan Shen, Shuqiang Wang
      Pages 146-157

    5. Classification of Event-Related Potential Signals with a Variant of UNet Algorithm Using a Large P300 Dataset

      • Maryam Khoshkhooy Titkanlou, Roman Mouček
      Pages 158-166

    6. Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research

      • Roshan Bhandari, Rishikesh V. Phatangare, Mark A. Eckert, Kenneth I. Vaden, James Z. Wang
      Pages 167-178

    7. Conversion from Mild Cognitive Impairment to Alzheimer’s Disease: A Comparison of Tree-Based Machine Learning Algorithms for Survival Analysis

      • Alessia Sarica, Federica Aracri, Maria Giovanna Bianco, Maria Grazia Vaccaro, Andrea Quattrone, Aldo Quattrone
      Pages 179-190
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Other volumes

  1. Brain Informatics

Editors and Affiliations

  • Stevens Institute of Technology, Hoboken, USA

    Feng Liu, Hongjun Wang

  • Lehigh University, Bethlehem, USA

    Yu Zhang

  • Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  • Boston University, Boston, USA

    Emily P. Stephen

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