Fatemeh Jamaloo
Papers
1
Total Citations
18
H-Index
1
About
Fatemeh Jamaloo’s research lies at the intersection of brain-computer interfaces (BCI), machine learning, and assistive technology, where she develops algorithms to decode neural signals for real-world applications. Her most cited work, “Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications” (2018, 18 citations), tackles a core challenge in BCI: reliably detecting event-related desynchronization patterns from electroencephalography (EEG) data. By fusing common spatial pattern (CSP) feature extraction with kernel linear discriminant analysis and generalized radial basis function classifiers, Jamaloo’s approach significantly improves the accuracy of motor imagery classification—a critical step toward controlling prosthetics, robotic limbs, and human-machine interfaces. This work builds on established methods to enhance real-time performance, demonstrating her ability to refine foundational techniques for practical impact. Though her citation count is modest, her contributions are notable for their methodological rigor and direct relevance to advancing non-invasive BCI systems. Jamaloo’s research exemplifies how careful algorithmic integration can push the boundaries of neural decoding, offering promising pathways for future assistive technologies that restore movement and communication for individuals with motor disabilities.
Research Focus
Key Achievements
Top Papers
- 1