Year |
Citation |
Score |
2021 |
Bhattacharyya S, Konar A, Raza H, Khasnobish A. Editorial: Brain-Computer Interfaces for Perception, Learning, and Motor Control. Frontiers in Neuroscience. 15: 758104. PMID 34744619 DOI: 10.3389/fnins.2021.758104 |
0.372 |
|
2021 |
Rathee D, Raza H, Roy S, Prasad G. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Scientific Data. 8: 120. PMID 33927204 DOI: 10.1038/s41597-021-00899-7 |
0.761 |
|
2019 |
Raza H, Rathee D, Zhou SM, Cecotti H, Prasad G. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing. 343: 154-166. PMID 32226230 DOI: 10.1016/J.Neucom.2018.04.087 |
0.721 |
|
2019 |
Devi SJ, Singh B, Raza H. Link Prediction Evaluation Using Palette Weisfeiler-Lehman Graph Labelling Algorithm International Journal of Knowledge and Systems Science. 10: 1-20. DOI: 10.4018/Ijkss.2019010101 |
0.313 |
|
2018 |
Chowdhury A, Raza H, Meena YK, Dutta A, Prasad G. An EEG-EMG Correlation-based Brain-Computer Interface for Hand Orthosis Supported Neuro-Rehabilitation. Journal of Neuroscience Methods. PMID 30452976 DOI: 10.1016/J.Jneumeth.2018.11.010 |
0.74 |
|
2018 |
Chowdhury A, Meena YK, Raza H, Bhushan B, Uttam AK, Pandey N, Hashmi AA, Bajpai A, Dutta A, Prasad G. Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability. Ieee Journal of Biomedical and Health Informatics. PMID 30080152 DOI: 10.1109/Jbhi.2018.2863212 |
0.703 |
|
2018 |
Chowdhury A, Raza H, Meena YK, Dutta A, Prasad G. Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation Ieee Transactions On Cognitive and Developmental Systems. 10: 1070-1080. DOI: 10.1109/Tcds.2017.2787040 |
0.638 |
|
2017 |
Rathee D, Raza H, Prasad G, Cecotti H. Current Source Density Estimation Enhances the Performance of Motor-Imagery related Brain-Computer Interface. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society. PMID 28715332 DOI: 10.1109/Tnsre.2017.2726779 |
0.706 |
|
2017 |
Chowdhury A, Raza H, Dutta A, Prasad G. EEG-EMG based Hybrid Brain Computer Interface for Triggering Hand Exoskeleton for Neuro-Rehabilitation Artificial Intelligence Review. 45. DOI: 10.1145/3132446.3134909 |
0.634 |
|
2015 |
Raza H, Cecotti H, Li Y, Prasad G. Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface Proceedings of the International Joint Conference On Neural Networks. 2015. DOI: 10.1109/IJCNN.2015.7280742 |
0.639 |
|
2015 |
Raza H, Cecotti H, Prasad G. Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces Proceedings of the International Joint Conference On Neural Networks. 2015. DOI: 10.1109/IJCNN.2015.7280737 |
0.618 |
|
2015 |
Raza H, Prasad G, Li Y. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments Pattern Recognition. 48: 659-669. DOI: 10.1016/J.Patcog.2014.07.028 |
0.342 |
|
2015 |
Raza H, Cecotti H, Li Y, Prasad G. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface Soft Computing. 1-12. DOI: 10.1007/s00500-015-1937-5 |
0.648 |
|
2014 |
O'Doherty D, Meena YK, Raza H, Cecotti H, Prasad G. Exploring gaze-motor imagery hybrid brain-computer interface design Proceedings - 2014 Ieee International Conference On Bioinformatics and Biomedicine, Ieee Bibm 2014. 335-339. DOI: 10.1109/BIBM.2014.6999180 |
0.646 |
|
2014 |
Raza H, Prasad G, Li Y, Cecotti H. Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces Proceedings - 2014 Ieee International Conference On Bioinformatics and Biomedicine, Ieee Bibm 2014. 230-236. DOI: 10.1109/BIBM.2014.6999160 |
0.666 |
|
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