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Atilla Kilicarslan

Showing results (1-10 of 12) with videos related to

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Journal of Neural Engineering|June 21, 2019
Characterization and real-time removal of motion artifacts from EEG signalsAtilla Kilicarslan, Jose Luis Contreras Vidal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 18, 2020
Towards a Unified Framework for De-noising Neural SignalsAtilla Kilicarslan, Jose L Contreras-Vidal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 18, 2020
Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural NetworksAlexander Craik, Atilla Kilicarslan, Jose L Contreras-Vidal
Journal of Neural Engineering|February 11, 2016
A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurementsAtilla Kilicarslan, Robert G Grossman, Jose Luis Contreras-Vidal
Frontiers in Neuroscience|April 20, 2017
Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG SignalsYuhang Zhang, Saurabh Prasad, Atilla Kilicarslan, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
High accuracy decoding of user intentions using EEG to control a lower-body exoskeletonAtilla Kilicarslan, Saurabh Prasad, Robert G Grossman, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reductionThomas C Bulea, Saurabh Prasad, Atilla Kilicarslan, et al.
Frontiers in Neuroscience|December 16, 2014
Sitting and standing intention can be decoded from scalp EEG recorded prior to movement executionThomas C Bulea, Saurabh Prasad, Atilla Kilicarslan, et al.
Journal of Visualized Experiments : Jove|August 6, 2013
Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decodingThomas C Bulea, Atilla Kilicarslan, Recep Ozdemir, et al.
Scientific Data|June 1, 2026
EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy IndividualsShantanu Sarkar, Kevin Nathan, Atilla Kilicarslan, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Journal of Neural Engineering|June 21, 2019
Characterization and real-time removal of motion artifacts from EEG signalsAtilla Kilicarslan, Jose Luis Contreras Vidal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 18, 2020
Towards a Unified Framework for De-noising Neural SignalsAtilla Kilicarslan, Jose L Contreras-Vidal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 18, 2020
Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural NetworksAlexander Craik, Atilla Kilicarslan, Jose L Contreras-Vidal
Journal of Neural Engineering|February 11, 2016
A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurementsAtilla Kilicarslan, Robert G Grossman, Jose Luis Contreras-Vidal
Frontiers in Neuroscience|April 20, 2017
Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG SignalsYuhang Zhang, Saurabh Prasad, Atilla Kilicarslan, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
High accuracy decoding of user intentions using EEG to control a lower-body exoskeletonAtilla Kilicarslan, Saurabh Prasad, Robert G Grossman, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reductionThomas C Bulea, Saurabh Prasad, Atilla Kilicarslan, et al.
Frontiers in Neuroscience|December 16, 2014
Sitting and standing intention can be decoded from scalp EEG recorded prior to movement executionThomas C Bulea, Saurabh Prasad, Atilla Kilicarslan, et al.
Journal of Visualized Experiments : Jove|August 6, 2013
Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decodingThomas C Bulea, Atilla Kilicarslan, Recep Ozdemir, et al.
Scientific Data|June 1, 2026
EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy IndividualsShantanu Sarkar, Kevin Nathan, Atilla Kilicarslan, et al.
Pageof 2