Vertaile menetelmiä

	BCI-liikemielikuvitus	EMG-verhokäyrä	Lihassynnergia-analyysi
Tieteenala	Biomekaniikka	Biomekaniikka	Biomekaniikka
Menetelmäperhe	Process / pipeline	Process / pipeline	Process / pipeline
Syntyvuosi	1999	1999	1999
Kehittäjä	Gert Pfurtscheller	Roberto Merletti	Marc Tresch
Tyyppi	Neural signal processing and decoding pipeline	Digital signal processing pipeline	Dimensionality reduction and pattern extraction
Alkuperäislähde	Pfurtscheller, G., & Neuper, C. (1999). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123-1134.	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.
Rinnakkaisnimet	Motor imagery BCI, MI-BCI, EEG motor decoding	EMG linear envelope, RMS envelope, Activation envelope	Motor synergy, Synergy extraction, Motor primitives
Liittyvät	3	3	3
Tiivistelmä	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.	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.