(2017). Current Source Density Estimation Enhances the Performance of Motor-Imagery related Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering (99) , 1 -1.
(2016). Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing 20 (8) , 3085 -3096.
(2015). EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48 (3) , 659 -669.
(2011). GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network. International Journal of Computer Applications 16 (3) , 13 -18. Book Chapters
(n.d.) EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments. In Artificial Intelligence Applications and Innovations.
(2015). Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces. doi: 10.1109/IJCNN.2015.7280737
(2015). Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface. doi: 10.1109/IJCNN.2015.7280742
(2015). A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation. doi: 10.1109/EMBC.2015.7319421
(2014). Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces. doi: 10.1109/BIBM.2014.6999160
(2013). Dataset Shift Detection in Non-stationary Environments Using EWMA Charts. doi: 10.1109/SMC.2013.537