Shahnawaz Qureshi
Papers
2
Total Citations
8
H-Index
2
About
Shahnawaz Qureshi is a researcher at the intersection of affective computing, human-robot interaction, and biomedical signal processing. His work focuses on enabling machines to perceive human emotional states through physiological signals, particularly electroencephalography (EEG). In his pioneering study, "An Empirical Study of Machine Learning Techniques for Classifying Emotional States from EEG Data" (2012, 6 citations), Qureshi systematically evaluated various classifiers for decoding emotional responses from brain activity, laying groundwork for more intuitive human-robot interfaces. He extended this line of inquiry in "Evaluation of Classifiers for Emotion Detection While Performing Physical and Visual Tasks: Tower of Hanoi and IAPS" (2018, 2 citations), where he compared emotion classification performance across cognitive and visual stimuli. Qureshi’s contributions are significant for developing robots that can adapt their behavior based on a user’s emotional state, moving beyond simple command execution toward truly responsive interaction. His work demonstrates that EEG-based emotion recognition can be robust across different task contexts, a crucial step for real-world deployment. By bridging machine learning, neuroscience, and robotics, Qureshi is helping to create a future where technology understands not just what we say, but how we feel.
Research Focus
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Top Papers
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