Publications

Journal Articles

  1. & 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.
  2. & Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing 20(8), 3085-3096.
  3. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48(3), 659-669.
  4. GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network. International Journal of Computer Applications 16(3), 13-18.

Book Chapters

  1. EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments. In Artificial Intelligence Applications and Innovations. -635).

Conference Contributions

  1. (2015). Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces. , 1-7. doi:10.1109/IJCNN.2015.7280737
  2. (2015). Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface. , 1-8. doi:10.1109/IJCNN.2015.7280742
  3. (2015). A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation. , 4610-4614. doi:10.1109/EMBC.2015.7319421
  4. (2014). Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces. , 230-236. doi:10.1109/BIBM.2014.6999160
  5. (2014). Exploring gaze-motor imagery hybrid brain-computer interface design. , 335-339. doi:10.1109/BIBM.2014.6999180
  6. (2014). Adaptive learning with covariate shift-detection for non-stationary environments. , 1-8. doi:10.1109/UKCI.2014.6930161
  7. (2013). Dataset Shift Detection in Non-stationary Environments Using EWMA Charts. , 3151-3156. doi:10.1109/SMC.2013.537
  8. (2010). Selection of cluster-head using PSO in CGSR protocol. , 91-94. doi:10.1109/ICM2CS.2010.5706725