Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis
Universiti Kebangsaan Malaysia（马来西亚国民大学）；Qatar University（卡塔尔大学）；Hamad General Hospital（哈马德综合医院）；Alkhor Hospital（阿尔霍尔医院）；BIRDEM General Hospital（BIRDEM综合医院）；North South University（南北大学）
Md Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal H. M. Ali, Ahmad Ashrif A. Bakar, Serkan Kiranyaz, Amith Khandakar, Mohammed Alhatou, … 展开作者
Md Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal H. M. Ali, Ahmad Ashrif A. Bakar, Serkan Kiranyaz, Amith Khandakar, Mohammed Alhatou, Rumana Habib, Muhammad Maqsud Hossain
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio (ΔSNR) and ii) Percentage reduction in motion artifacts (η). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average ΔSNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average η (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average ΔSNR and η values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average ΔSNR (16.55 dB, utilizing db1 wavelet packet) and largest average η (41.40%, using fk8 wavelet packet). The highest average ΔSNR and η using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.
Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology（麻省理工学院哈佛-麻省理工健康科学与技术系）；Affective Computing group, MIT Media Lab, Massachusetts Institute of Technology（麻省理工学院媒体实验室情感计算研究组）；Center for Pain and the Brain, Harvard Medical School（哈佛医学院疼痛与大脑研究中心）；Department of Anesthesiology, Boston Children’s Hospital（波士顿儿童医院麻醉科）
NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University（西溪大学工程学院先进神经技术中心）；School of Data Science, Xiamen University Malaysia（马来西亚厦门大学数据科学学院）；Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第二医院神经外科）；Department of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center（浙江省公检强制隔离戒毒所教育矫正部）；Zhejiang Liangzhu Compulsory Isolated Detoxification Center（浙江省良渚强制隔离戒毒所）
EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
University of Applied Science and Technology（应用科技大学）；MZG Consulting（MZG咨询公司）；Institute of Electrical and Electronics Engineers（电气与电子工程师协会）；Baku State University（巴库国立大学）
Investigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, 95123 Catania, Italy（意大利卡塔尼亚大学电气、电子与计算机工程系）；Department of Engineering, University of Palermo, 90128 Palermo, Italy（意大利巴勒莫大学工程系）
A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island（电气、计算机与生物医学工程系，罗德岛大学）；Department of Mechanical Engineering, University of Rhode Island（机械工程系，罗德岛大学）
RatioWaveNet: A Learnable RDWT Front-End for Robust and Interpretable EEG Motor-Imagery Classification
Department of Electrical, Electronics and Informatics Engineering, University of Catania（电子、电子与信息工程系，卡塔尼亚大学）；Department of Engineering, University of Palermo（工程系，巴勒莫大学）
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
K. N. Toosi University of Technology（K. N. 图西理工大学）；Deakin University（迪肯大学）；University of Turku（图尔库大学）；Effat University（埃法特大学）；Ngee Ann Polytechnic（义安理工学院）；SUSS University（新跃社科大学）；Asia University（亚洲大学）
GCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding
Faculty of frontier sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；Doumaomao NetTech Co., Ltd.（杜马莫莫网络技术有限公司）；University of Melbourne（墨尔本大学）；The University of New South Wales（新南威尔士大学）
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Computer Science Department, College of Science, University of Zakho（扎赫大学科学学院计算机科学系）；Department of Computer Science and Information Technology, The American University of Kurdistan（库尔德斯坦美国大学计算机科学与信息技术系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学科学研究中心PRIME实验室）
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis
Rashtriya Raksha University（印度国家防卫大学）；National Institute of Technology, Durgapur（杜尔加普尔国家理工学院）；Guru Nanak Institute of Technology（古鲁那纳克技术学院）；University of Malta（马耳他大学）
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems
Salim Habib University（萨利姆哈比布大学）；NUST（国家科技大学）；Riphah International University（里法国际大学）；NED University（NED大学）；Institute of Business Administration（商业管理学院）
