Schmidt, Marie D.; Iossifidis, Ioannis The Spatial and~Temporal Resolution of~Motor Intention in~Multi-target Prediction Proceedings Article In: Neumann, Philipp; Puma, Michael J.; Lees, Michael H.; Groen, Derek; Dongarra, Jack J.; Sloot, Peter M. A. (Hrsg.): Computational Science – ICCS 2026, S. 421–428, Springer Nature Switzerland, Cham, 2026, ISBN: 978-3-032-29924-6. Abstract | Links | BibTeX | Schlagwörter: BCI, EMG, HMI, Motor control, Random Forest2026
1.
@inproceedings{schmidtSpatialTemporalResolution2026,
title = {The Spatial and~Temporal Resolution of~Motor Intention in~Multi-target Prediction},
author = {Marie D. Schmidt and Ioannis Iossifidis},
editor = {Philipp Neumann and Michael J. Puma and Michael H. Lees and Derek Groen and Jack J. Dongarra and Peter M. A. Sloot},
doi = {10.1007/978-3-032-29924-6_36},
isbn = {978-3-032-29924-6},
year = {2026},
date = {2026-06-27},
urldate = {2026-06-27},
booktitle = {Computational Science – ICCS 2026},
pages = {421–428},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Reaching, grasping, and object manipulation are essential motor functions in everyday life. This study predicts movement direction and target location from multichannel electromyography (EMG) signals, examining how spatially and temporally accurate intentions can be detected relative to movement onset. A computational pipeline combining data-driven temporal segmentation with Random Forest model is applied to EMG data across planning, execution, and contact phases of a reaching task.},
keywords = {BCI, EMG, HMI, Motor control, Random Forest},
pubstate = {published},
tppubtype = {inproceedings}
}
Reaching, grasping, and object manipulation are essential motor functions in everyday life. This study predicts movement direction and target location from multichannel electromyography (EMG) signals, examining how spatially and temporally accurate intentions can be detected relative to movement onset. A computational pipeline combining data-driven temporal segmentation with Random Forest model is applied to EMG data across planning, execution, and contact phases of a reaching task.