विधियों की तुलना करें

	सामान्य स्थानिक पैटर्न	मार्कर रहित गति कैप्चर
क्षेत्र	जैव यांत्रिकी	जैव यांत्रिकी
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2000	2017
प्रवर्तक	Herbert Ramoser	Zhe Cao
प्रकार	Spatial filtering and feature extraction	Deep learning pipeline
मौलिक स्रोत	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).
उपनाम	CSP, Spatial filtering, CSP decomposition	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation
संबंधित	3	3
सारांश	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.