PA-TCNet improves cross-subject motor imagery EEG decoding accuracy in stroke patients to 66.56% and 72.75% on two datasets by pathology-aware rhythmic state modeling and physiology-constrained pseudo-label refinement.
Classification of motor imagery performance in acute stroke.Journal of Neural Engineering, 11(3):036001, apr 2014
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
PA-TCNet improves cross-subject motor imagery EEG decoding accuracy in stroke patients to 66.56% and 72.75% on two datasets by pathology-aware rhythmic state modeling and physiology-constrained pseudo-label refinement.