BrainNet

作者：Junru Chen, Yang Yang, Tao Yu, Yingying Fan, Xiaolong Mo, Carl Yang · 发表于：Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · 年份：2022 · DOI：10.1145/3534678.3539178 · 被引用次数：21 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、Time Series Analysis and Forecasting

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the patient's brain. Existing works have begun to employ machine learning models to detect epileptic waves via cortical electroencephalogram (EEG), which refers to brain data obtained from a noninvasive examination performed on the patient's scalp surface to record electrical activity in the brain. However, the recently developed stereoelectrocorticography (SEEG) method provides information in stereo that is more precise than conventional EEG, and has been broadly applied in clinical practice. Therefore, in this paper, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. While offering new opportunities, SEEG also poses several challenges. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. These propagation paths, also known as the epileptogenic network, are deemed to be a key factor in the context of epilepsy surgery. However, the question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience. Moreover, the nature of epileptic waves and SEEG data inevitably leads to extremely imbalanced labels and severe noise. To address these challenges, we propose a novel model (BrainNet) tha...