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Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface.

, , , and . IJCNN, page 1-8. IEEE, (2015)

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EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments., , and . Pattern Recognition, 48 (3): 659-669 (2015)Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface., , , and . IJCNN, page 1-8. IEEE, (2015)The Use of Dynamic and Static Characteristics of Gait for Individual Identification., , and . IMVIP, page 111-116. IEEE Computer Society, (2009)Self-paced brain-controlled wheelchair methodology with shared and automated assistive control., , and . CCMB, page 120-127. IEEE, (2011)Development of a hybrid PCA-ANFIS measurement system for monitoring product quality in the coating industry., , , and . SMC (4), page 3519-3524. IEEE, (2004)A design for a self-organizing fuzzy neural network based on the genetic algorithm., , and . SMC, page 1967-1972. IEEE, (2003)Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface., , , , and . CoRR, (2018)Quantum Neural Network-Based EEG Filtering for a Brain-Computer Interface., , , , and . IEEE Trans. Neural Netw. Learning Syst., 25 (2): 278-288 (2014)Individual Identification Using Gait Sequences under Different Covariate Factors., , and . ICVS, volume 5815 of Lecture Notes in Computer Science, page 84-93. Springer, (2009)Hand-Exoskeleton Assisted Progressive Neurorehabilitation Using Impedance Adaptation Based Challenge Level Adjustment Method., , , , and . IEEE Trans. Haptics, 12 (2): 128-140 (2019)