השוואת שיטות

	תבנית מרחבית נפוצה	מעטפת EMG	ניתוח סינרגיות שרירים
תחום	ביומכניקה	ביומכניקה	ביומכניקה
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2000	1999	1999
הוגה השיטה	Herbert Ramoser	Roberto Merletti	Marc Tresch
סוג	Spatial filtering and feature extraction	Digital signal processing pipeline	Dimensionality reduction and pattern extraction
מקור מכונן	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.	Phinyomark, A., Quaine, F., Charbonnier, S., & Serviere, C. (2012). Robust EMG feature extraction in the whitespace. IEEE Transactions on Biomedical Engineering, 59(5), 1505-1517.	Tresch, M. C., Saltiel, P., Bizzi, E., & Bizzi, E. (1999). The construction of movement by the spinal cord. Nature Neuroscience, 2(2), 162-167.
כינויים	CSP, Spatial filtering, CSP decomposition	EMG linear envelope, RMS envelope, Activation envelope	Motor synergy, Synergy extraction, Motor primitives
קשורות	3	3	3
תקציר	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.	Electromyography (EMG) envelope analysis extracts the amplitude modulation of muscle electrical activity to quantify muscle activation over time. By filtering and demodulating the raw EMG signal, practitioners obtain a smoothed activation profile that reflects when and how intensely a muscle is contracting during movement or fatigue.	Muscle synergy analysis decomposes complex motor behavior into a small set of coactivated muscle groups (synergies or motor primitives). Pioneered by Marc Tresch and colleagues studying frog motor control, this approach reveals how the nervous system simplifies the control of many muscles by organizing them into task-relevant combinations.