Ghulam Muhammad
Papers
13
Total Citations
1,142
H-Index
11
About
Ghulam Muhammad is a leading researcher at the intersection of artificial intelligence, brain-computer interfaces (BCI), and intelligent robotics. His work primarily focuses on decoding EEG signals for motor imagery, where he has developed innovative deep learning architectures—including attention-based Inception models and dynamic convolution with multilevel attention—to improve the accuracy and robustness of BCI systems for assistive technologies. His comprehensive review on deep learning for EEG motor imagery classification has garnered over 558 citations, underscoring its foundational impact in the field. Beyond neural decoding, Muhammad has made significant contributions to agricultural robotics, creating a deep learning-based vision system for date fruit classification and harvesting, supported by a publicly available dataset that has accelerated research in automated agriculture. His work extends to telesurgery robots enabled by 5G tactile internet and intelligent industrial catching robots for Industry 4.0 logistics. With multiple papers exceeding 200 citations and a portfolio spanning healthcare, agriculture, and manufacturing, Ghulam Muhammad’s research exemplifies how AI and robotics can transform real-world applications, from restoring mobility to revolutionizing food production.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Telesurgery Robot Based on 5G Tactile Internet87 citations · 2018
- 4
- 5Date fruit dataset for intelligent harvesting69 citations · 2019
- 6Multi-CNN Feature Fusion for Efficient EEG Classification38 citations · 2020
- 7Attention based Inception model for robust EEG motor imagery classification32 citations · 2021
- 8
- 9
- 10