Сравнение методов

	Моторная визуализация в интерфейсах мозг-компьютер (ИМК)	Общий пространственный шаблон	Огибающая ЭМГ
Область	Биомеханика	Биомеханика	Биомеханика
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1999	2000	1999
Автор метода	Gert Pfurtscheller	Herbert Ramoser	Roberto Merletti
Тип	Neural signal processing and decoding pipeline	Spatial filtering and feature extraction	Digital signal processing pipeline
Основополагающий источник	Pfurtscheller, G., & Neuper, C. (1999). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123-1134.	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.
Другие названия	Motor imagery BCI, MI-BCI, EEG motor decoding	CSP, Spatial filtering, CSP decomposition	EMG linear envelope, RMS envelope, Activation envelope
Связанные	3	3	3
Сводка	Brain-computer interface (BCI) using motor imagery decodes the intent to move from brain activity (typically EEG) recorded while subjects imagine movement without actual muscle contraction. Pioneered by Gert Pfurtscheller and colleagues, motor imagery BCIs enable communication and control for paralyzed patients and enhance motor learning in rehabilitation.	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.