Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach: We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI. Results: The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins. Conclusion and significance: Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable, economical, fast and low-latency BCIs.
Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution
Picower Institute, Massachusetts Institute of Technology（麻省理工学院皮考尔研究所）；Institute for Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；MIT/MGH Brain Arousal State Control Innovation Center, Massachusetts Institute of Technology（麻省理工学院MIT/MGH脑觉醒状态控制创新中心）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院阿西诺拉·A·马蒂诺斯生物医学成像中心）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Nanyang Technological University（南洋理工大学）；National University Hospital（国立大学医院）；Tan Tock Seng Hospital（陈笃生医院）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（新加坡资讯通信研究院（新加坡科技研究局））；Indian Institute of Technology, Palakkad（印度理工学院巴拉卡德分校）；Korea University（高丽大学）
A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram
University of Washington（华盛顿大学）；University of California, Irvine（加利福尼亚大学尔湾分校）；VA Portland Health Care System（波特兰退伍军人医疗保健系统）；Oregon Health & Science University（俄勒冈健康与科学大学）
University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）；Monash University（莫纳什大学）；Kharazmi University（哈拉齐米大学）；CNRS-Université de Toulouse 3（法国国家科学研究中心-图卢兹第三大学）
Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；University of Sheffield（谢菲尔德大学）；School of Automation, Beijing University of Posts and Telecommunications（北京邮电大学自动化学院）
Alleviating Seasickness through Brain-Computer Interface-based Attention Shift
School of Automation Science and Engineering（自动化科学与工程学院）；South China University of Technology（华南理工大学）；Research Center for Brain–Computer Interface（脑机接口研究中心）；Pazhou Laboratory（琶洲实验室）；School of Artificial Intelligence（人工智能学院）；South China Normal University（华南师范大学）；School of Psychology（心理学学院）；South China Brain–Computer Interface Technology Co., Ltd.（华南脑机接口技术有限公司）
A Transformer-based deep neural network model for SSVEP classification
Southwest University of Science and Technology（西南科技大学）；University of Electronic Science and Technology of China（电子科技大学）；Nanyang Technological University（南洋理工大学）
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
University of Padova（帕多瓦大学）；National Centre for Neurology and Psychiatry（国家神经与精神病学中心）；National Inter-University Consortium for Telecommunications (CNIT)（全国大学间电信联盟（CNIT））；University of Coimbra（科英布拉大学）；Polytechnic Institute of Tomar（托马尔理工学院）
