Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
U.S. Army Research Laboratory（美国陆军研究实验室）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, Jean M. Vettel
备注：
Accepted for publication at the Journal of Neural Engineering
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for any domain-specific knowledge or calibration data. We report across subject mean accuracy of approximately 80% (chance being 8.3%) and show this is substantially better than current state-of-the-art hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we analyze our Compact-CNN to examine the underlying feature representation, discovering that the deep learner extracts additional phase and amplitude related features associated with the structure of the dataset. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学）
Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；School of Computer Science and Technology, Beihang University（计算机科学与技术学院，北航）
Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data
School of Control Science（控制科学学院）；Engineering Shandong University Jinan, Shandong（山东大学工程学院济南山东）；Shandong Inspur Science Research Institute Co., Ltd. Jinan, Shandong（山东 Inspur 科研院有限公司济南山东）
Explainability of Recurrent Neural Networks for Enhancing P300-based Brain-Computer Interfaces
Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid（生物神经计算组，信息工程系，理工大学高级学院，马德里自治大学）；Grupo de Investigación en Electrónica y Telemática, Universidad Politécnica Salesiana（电子与电信研究组，萨利纳斯理工大学）
LI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding
School of Biomedical Engineering, Tsinghua Laboratory of Brain and Intelligence, and IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学生物医学工程学院、清华脑与智能实验室、IDG/麦戈文脑科学研究院）；School of Life Sciences, Tsinghua University（清华大学生命科学学院）；Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Department of Psychological and Cognitive Sciences, Tsinghua University（清华大学心理与认知科学系）
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System
University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究院）；School of Computer Science, Faculty of Engineering and Information Technology（工程与信息技术学院计算机科学学院）
