Brain-computer interface (BCI) is used for communication between humans and devices by recognizing status and intention of humans. Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain. In particular, the control of drone swarms (the direction and formation) has more advantages compared to the control of a drone. The visual imagery (VI) paradigm is that subjects visually imagine specific objects or scenes. Reduction of the variability among EEG signals of subjects is essential for practical BCI-based systems. In this study, we proposed the subepoch-wise feature encoder (SEFE) to improve the performances in the subject-independent tasks by using the VI dataset. This study is the first attempt to demonstrate the possibility of generalization among subjects in the VI-based BCI. We used the leave-one-subject-out cross-validation for evaluating the performances. We obtained higher performances when including our proposed module than excluding our proposed module. The DeepConvNet with SEFE showed the highest performance of 0.72 among six different decoding models. Hence, we demonstrated the feasibility of decoding the VI dataset in the subject-independent task with robust performances by using our proposed module.
NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
University of Minnesota（明尼苏达大学）；Alljoined；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Stability AI/Medical AI Research Center (MedARC)（Stability AI/医疗AI研究中心（MedARC））
Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Ningbo HwaMei Hospital, UCAS（中国科学院大学宁波华美医院）
Progress Towards Decoding Visual Imagery via fNIRS
McGill University（麦吉尔大学）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Medical AI Research Center (MedARC)（医学人工智能研究中心）；Stability AI；University of Waterloo（滑铁卢大学）
Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
Ministry of Education Center for Studies of Psychological Application（教育部心理应用研究中心）；Center for Studies of Psychological Application（心理应用研究中心）；Key Laboratory of Brain, Cognition and Education Sciences（脑认知与教育科学重点实验室）；School of Psychology（心理学学院）；Guangdong Key Laboratory of Mental Health and Cognitive Science（广东省心理健康与认知科学重点实验室）
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
Universidad Publica de Navarra（纳瓦拉公立大学）；Institute of Smart Cities, Universidad Publica de Navarra（纳瓦拉公立大学智慧城市研究院）；Hospital Complex of Navarre (CHN)（纳瓦拉综合医院）；Slovak University of Technology in Bratislava（布拉迪斯拉发斯洛伐克工业大学）；University of Technology Sydney（悉尼科技大学）
Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping
Shandong University of Science and Technology（山东科技大学）；Technical University of Munich（慕尼黑工业大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Iceland（冰岛大学）
Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Gamania Digital Entertainment Co., Ltd.（Gamania数字娱乐有限公司）；Institute of Information Science, Academia Sinica（Academia Sinica信息科学研究所）；Sony Computer Science Laboratories, Inc.（索尼计算机科学实验室有限公司）
MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Nanjing University of Science and Technology（南京理工大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
Brain Captioning: Decoding human brain activity into images and text
University of Rome Tor Vergata（罗马第二大学）；CNRS（法国国家科学研究中心）；Universite de Toulouse（图卢兹大学）；ANITI（图卢兹人工智能与自然智能研究所）；Martinos Center For Biomedical Imaging（马丁诺斯生物医学成像中心）；MGH（麻省总医院）；Harvard Medical School（哈佛医学院）
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
University of Rome, Tor Vergata（罗马第二大学）；Istituto di Bioimmagini e Fisiologia Molecolare, CNR（意大利国家研究委员会生物成像与分子生理学研究所）；Martinos Center for Biomedical Imaging（马丁诺斯生物医学成像中心）；MGH（麻省总医院）；Harvard Medical School（哈佛医学院）
SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
IPAI, Seoul National University（韩亚国立大学IPAI）；ECE, Seoul National University（韩亚国立大学电子工程系）；Psychology, Seoul National University（韩亚国立大学心理学系）；Brookhaven National Lab（布鲁赫斯国家实验室）；ASRI / INMC / AIIS, Seoul National University（韩亚国立大学ASRI/INMC/AIIS）
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli
Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；School of Computer Science and Information Engineering, Hefei University of Technology (HFUT)（合肥工业大学计算机与信息工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Zhejiang University（浙江大学）；Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院澳大利亚人工智能研究所）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）
Tongji University（同济大学）；University of Oxford（牛津大学）；North China Institute of Computing Technology, State Information Center of China（国家信息中心华北计算技术研究所）；Macquarie University（麦考瑞大学）
MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Agency for Science, Technology and Research（科技研究局）；New York University（纽约大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）
