M
Muhammad Jawad Khan
Researcher at University of the Sciences
Publications - 41
Citations - 791
Muhammad Jawad Khan is an academic researcher from University of the Sciences. The author has contributed to research in topics: Computer science & Joint attention. The author has an hindex of 9, co-authored 34 publications receiving 436 citations. Previous affiliations of Muhammad Jawad Khan include National University of Science and Technology & Pusan National University.
Papers
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Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.
TL;DR: Non-invasive hybrid brain–computer interface technologies for improving classification accuracy and increasing the number of commands are reviewed and the future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.
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Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.
TL;DR: The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG–fNIRS interface.
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An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children
Sara Ali,Faisal Mehmood,Darren Dancey,Yasar Ayaz,Muhammad Jawad Khan,Noman Naseer,Rita De Cassia Amadeu,Haleema Sadia,Raheel Nawaz +8 more
TL;DR: A novel mathematical model based on an adaptive multi-robot therapy of ASD children focusing on two main impairments in autism, with significant improvements in both modules, along with a notable improvement in multi-communication skills of the participating children.
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Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.
Umer Asgher,Khurram Khalil,Muhammad Jawad Khan,Riaz Ahmad,Shahid Ikramullah Butt,Yasar Ayaz,Yasar Ayaz,Noman Naseer,Salman Nazir +8 more
TL;DR: Novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem.
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Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study
Rayyan Azam Khan,Noman Naseer,Sajid Saleem,Nauman Khalid Qureshi,Farzan Majeed Noori,Muhammad Jawad Khan +5 more
TL;DR: These results show the feasibility of using hrf for effective removal of noises from fNIRS data and the best optimal filter for a specific cortical task owing to a specific cortex region.