EEG Signal Analysis

EEG signal analysis aims to extract meaningful information from brain electrical activity, primarily for understanding cognitive processes and developing brain-computer interfaces (BCIs). Current research emphasizes improving the accuracy and robustness of EEG analysis by addressing challenges like noise and inter-subject variability, focusing on advanced deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs), often incorporating techniques like contrastive learning and self-supervised pretraining. These advancements hold significant potential for improving clinical diagnostics (e.g., seizure detection, depression diagnosis), enhancing BCIs, and furthering our understanding of brain function.

Papers

October 22, 2023

Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
Zhibo Zhang, Pengfei Li, Ahmed Y. Al Hammadi, Fusen Guo, Ernesto Damiani, Chan Yeob Yeun
Federated Learning EEG Data Threat Word EEG Classification EEG Signal Analysis EEG Datasets

June 29, 2023

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
Yunpeng Bai, Xintao Wang, Yan-pei Cao, Yixiao Ge, Chun Yuan, Ying Shan
Image Generation Brain Electroencephalography Brain Signal EEG Signal Analysis

May 14, 2023

Using EEG Signals to Assess Workload during Memory Retrieval in a Real-world Scenario
Kuan-Jung Chiang, Steven Dong, Chung-Kuan Cheng, Tzyy-Ping Jung
EEG Signal EEG Signal Analysis Memory Retrieval Heavy Workload Cognitive Ergonomics

May 3, 2023

HappyQuokka System for ICASSP 2023 Auditory EEG Challenge
Zhenyu Piao, Miseul Kim, Hyungchan Yoon, Hong-Goo Kang
EEG Signal Analysis Speech Envelope Signal Processing Grand Challenge Single Subject

April 21, 2023

Generate your neural signals from mine: individual-to-individual EEG converters
Zitong Lu, Julie D. Golomb
EEG Signal Analysis Neural Signal

March 20, 2023

Relate auditory speech to EEG by shallow-deep attention-based network
Fan Cui, Liyong Guo, Lang He, Jiyao Liu, ErCheng Pei, Yujun Wang, Dongmei Jiang
EEG Signal Attention Based Deep EEG Signal Analysis Attention Based Network Relation Information Attention Based Correlation Module Deep Similarity

March 9, 2023

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals
Abdolkarim Saeedi, Arash Maghsoudi, Fereidoun Nowshiravan Rahatabad
Recurrent Neural Network LSTM Network EEG Signal Analysis CNN LSTM Drug Response Prediction Major Depressive Disorder

November 9, 2022

Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning
Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Peter W. Kaplan, Wan Yee Kong, Ioannis Karakis, Aline Herlopian, Lakshman Arcot Jayagopal, Olga Taraschenko, Olga Selioutski, Gamaleldin Osman, Daniel Goldenholz, Cynthia Rudin, M. Brandon Westover
Deep Learning Model Brain Electroencephalography EEG Signal Analysis EEG Pattern

April 14, 2022

EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
Abbas Salami, Javier Andreu-Perez, Helge Gillmeister
Neural Network EEG Signal Analysis Motor Imagery Classification Eeg GPT

March 18, 2022

Analyzing EEG Data with Machine and Deep Learning: A Benchmark
Danilo Avola, Marco Cascio, Luigi Cinque, Alessio Fagioli, Gian Luca Foresti, Marco Raoul Marini, Daniele Pannone
Deep Learning New Benchmark New Machine EEG Classification EEG Signal Analysis

December 28, 2021

Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
Tiehang Duan, Zhenyi Wang, Sheng Liu, Sargur N. Srihari, Hui Yang
Brain Computer Interface Constructive Reduction EEG Signal EEG Signal Analysis Detection of Uncertainty in Exceedance Neural Decoding