Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way. Deep learning algorithms are capable of learning flexible nonlinear functions directly from data, and their constant processing latency is perfect for their deployment into online BCI systems. However, it is crucial for the jitter of the processing system to be as low as possible, in order to avoid unpredictable behaviour that can ruin the system's overall usability. In this paper, we present a novel encoding method, based on on deep convolutional autoencoders, that is able to perform efficient compression of the raw EEG inputs. We deploy our model in a ROS-Neuro node, thus making it suitable for the integration in ROS-based BCI and robotic systems in real world scenarios. The experimental results show that our system is capable to generate meaningful compressed encoding preserving to original information contained in the raw input. They also show that the ROS-Neuro node is able to produce such encodings at a steady rate, with minimal jitter. We believe that our system can represent an important step towards the development of an effective BCI processing pipeline fully standardized in ROS-Neuro framework.
Inclusive Brains（包容大脑）；Wavestone；Human Technology Foundation（人类科技基金会）；Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学）；Institute for Artificial Intelligence, Biotech Dental Group（人工智能研究所，生物技术牙科集团）；IBM Technology（IBM技术）；IBM France Lab（IBM法国实验室）
CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部长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）；Zhongguancun Academy（中关村学院）
DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI
State Key Laboratory of Brain-machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；MOE Frontier Science Center for Brain Science and Brain-machine Integration（教育部脑科学与脑机集成前沿科学中心）
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Key Laboratory of the Ministry of Education for 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）；Zhongguancun Academy（中关村学院）
Atoms of Thought: Universal EEG Representation Learning with Microstates
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；School of Clinical Medicine, Tsinghua University（清华大学临床医学院）；Beijing Five Seasons Medical Technology Co., Ltd.（北京五 Seasons 医疗科技有限公司）
