Marjan Saadati
Papers
2
Total Citations
40
H-Index
2
About
Marjan Saadati is a leading researcher at the intersection of cognitive neuroscience and artificial intelligence, specializing in mental workload assessment using functional near-infrared spectroscopy (fNIRS). Her work is foundational to designing adaptive human-computer interfaces that enhance safety and performance in high-stakes fields like aerospace and robotic surgery. Saadati’s major contributions include pioneering the use of convolutional neural networks (CNNs) to classify mental workload from spatial representations of fNIRS recordings, as demonstrated in her highly cited 2019 paper (32 citations). She further advanced the field by applying recurrent convolutional neural networks (RCNNs) for dynamic workload assessment in her 2021 study (8 citations), enabling more accurate, real-time monitoring of cognitive states. Her innovative fusion of deep learning with neuroimaging techniques has set new benchmarks for noninvasive cognitive state detection, directly impacting the development of safer, more responsive human-machine systems. Saadati’s work is essential reading for researchers in neuroengineering, human factors, and AI-driven interface design, offering a clear path toward adaptive systems that anticipate and respond to operator cognitive load.
Research Focus
Key Achievements
Top Papers
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