چکیده مقاله
Artifact detection is an essential step in the preprocessing of electrophysiological signals such as ECoG, as artifacts can significantly distort analysis and lead to misleading conclusions Traditionally, this task is performed manually by neuroscientists, which is both time consuming and impractical for large scale datasets This work introduces an unsupervised and automatic method for artifact detection using the DBSCAN clustering algorithm The approach is based on 11 extracted features and does not require any labeled data or human supervision Despite its simplicity, the method has shown strong performance in identifying artifacts across recordings, as confirmed through visual inspection This makes it a practical solution for efficient and scalable neural data preprocessing
کلیدواژهها
نویسندگان
Arash Narimani
۱ M Sc in Electronic Engineering
شیوه ارجاع
Narimani, Arash,1403,Unsupervised Artifact Detection From ECoG(iEEG) Signals Using DBSCAN,The 24th National Conference on Civil Engineering, the 24th National Conference on Electrical, Computer and Urban Engineering Engineering,Shirvan
ارائهشده در
مجموعه مقالات بیست و چهارمین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک18 دی 1403 · شیروان