2023.4.12 1:30-4:30 PM 本部7号楼210会议室 报告1：脑机接口针对对神经类疾病的康复和治疗的应用研究 摘要：针对脑瘫患者的可穿戴式脑机交互辅助交流设备的开发与应用，针对帕金森病冻结步态的闭关神经回路脑机接口调整机制的探索，针对癫痫患者致痫灶溯源的机器学习算法探索，针对情感障碍患者的恐惧预期探索。 报告人简介：赵海丰博士，上海交通大学医学院附属瑞金医院博士后，悉尼大学生物医学工程博士，北京交通大学软件工程学士，悉尼大学软件工程学士。主要研究方向为脑机接口，神经科学，神经计算学，公开发表学术文章6篇，其中SCI论文3篇，专著一篇，被引超50次。以报告形式参加国际会议2次，在地中海医学生物工程计算大会中参加脑机接口算法大赛，带队获得前十名的成绩。博士期间开发的可穿戴式系统，获得国际专利。 报告2：人工智能在癫痫管理中的高阶应用 Abs：Epilepsy has a significant adverse impact on almost 1% of people’s health and well-being globally. Clinical EEG monitoring devices that enable seizure onset detection and prediction are crucial for epilepsy patients to manage their seizure disorders. In the past three decades, many epileptic seizure detecting and forecasting methods have been developed and reported high performance. However, most of them are retrospective and lack continental and multi-dataset generalization, transparency, and reproducibility, making them hard to implement into clinical utility. Besides, the seizure prediction biomarker is yet to be fully answered, and this issue significantly limits clinician trust when using the seizure prediction algorithms. In this thesis, we propose a generalized epileptic seizure detection AI-assisted system that tested on a large scale of the clinical EEG dataset and proved to improve time efficiency while accuracy alongside the human expert. The seizure detection performance is further improved by combining EEG and ECG using a novel multimodal AI system and achieving the state-of-the-art AUC score on the largest public dataset (Temple University Hospital). Secondly, we propose a Bayesian convolutional neural network to facilitate the exploration of potential seizure forecasting biomarkers. Another problem we address is the need for long recording labeled EEG data for seizure forecasting. We propose a novel real-time seizure forecasting AI system that learns from the on-the-fly weak label generated by the detection model. Ultimately, we focus on developing a low-power, hardware-friendly implementation method using neuromorphic-compatible Spiking Neural Networks (SNNs) for seizure detection. 报告人简介： 杨壹凯，悉尼大学人工智能医疗博士，悉尼大学本科专业第一，获本科毕业最高荣誉Univeristy Medal（获得超10项本科奖学金）, 获得澳大利亚最高政府奖学金RTP International. 3 年半完成博士学位（AI 医疗），发表超10篇AI 应用论文，其中第一作者发表于AI 应用顶刊中科院一区（expert systems with application， IEEE JBHI 等）。2020 年带领团队获世界AI 癫痫预测比赛亚军，曾就职于澳大利亚科学院，澳大利亚皇家医院，现就职于国际知名工程公司Laing O'Rourke 人工智能子公司presien 高级人工智能工程师。兼任中国早期创投奇绩创坛Fellow, 硅谷创投on deck Fellow. 澳洲创业公司Ipomoea, Veritas-tech 联合创始人。
