পদ্ধতির তুলনা করুন

	কমন স্পেশাল প্যাটার্ন	মার্কারবিহীন গতি ধারণ (Markerless Motion Capture)
ক্ষেত্র	জৈব বলবিদ্যা	জৈব বলবিদ্যা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000	2017
প্রবর্তক	Herbert Ramoser	Zhe Cao
ধরন	Spatial filtering and feature extraction	Deep learning pipeline
মৌলিক উৎস	Ramoser, H., Mueller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.	Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
অপর নাম	CSP, Spatial filtering, CSP decomposition	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation
সম্পর্কিত	3	3
সারসংক্ষেপ	Common Spatial Pattern (CSP) is a spatial filtering technique that identifies electrode combinations that maximize the variance difference between two classes of EEG activity, typically used in brain-computer interfaces to enhance motor imagery discrimination. Introduced by Ramoser and colleagues in 2000, CSP has become a standard feature extraction method in BCI research.	Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications.