I
Imran Khan Niazi
Researcher at New Zealand College of Chiropractic
Publications - 151
Citations - 3209
Imran Khan Niazi is an academic researcher from New Zealand College of Chiropractic. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 24, co-authored 124 publications receiving 2242 citations. Previous affiliations of Imran Khan Niazi include Auckland University of Technology & University of Auckland.
Papers
More filters
Journal ArticleDOI
EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning
TL;DR: Experiments show that good classification results are obtained using the proposed methodology for the classification of EEG signals, and the proposed method compares favorably to other state-of-the-art feature extraction methods.
Journal ArticleDOI
Detection of movement intention from single-trial movement-related cortical potentials
Imran Khan Niazi,Ning Jiang,Olivier Tiberghien,Jørgen Feldbæk Nielsen,Kim Dremstrup,Dario Farina +5 more
TL;DR: Although TPR decreased with motor imagination in healthy subject and with attempted movements in stroke patients, the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.
Journal ArticleDOI
Precise temporal association between cortical potentials evoked by motor imagination and afference induces cortical plasticity.
TL;DR: The naturally generated brain activation when a person imagines a simple movement is used and combined with the afferent inflow that would be generated had the movement been performed rather than imagined to open up the possibilities to alter afferent‐generated feedback depending on the demands of the movement to be performed.
Journal ArticleDOI
Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface
Natalie Mrachacz-Kersting,Ning Jiang,Andrew James Thomas Stevenson,Imran Khan Niazi,Vladimir S. Kostic,Aleksandra M. Pavlović,Sasa Radovanovic,Milica Djuric-Jovicic,Federica Agosta,Kim Dremstrup,Dario Farina +10 more
TL;DR: In this paper, the authors evaluated the effect and underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design, where two groups of patients were divided into two groups: BCI-associative and BCInon-Associative groups.
Journal ArticleDOI
Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
Muhammad Zia ur Rehman,Asim Waris,Syed Omer Gilani,Mads Jochumsen,Imran Khan Niazi,Mohsin Jamil,Mohsin Jamil,Dario Farina,Ernest Nlandu Kamavuako +8 more
TL;DR: CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features, and this data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.