Somsirsa Chatterjee

About

Dr. Somsirsa Chatterjee is a leading researcher in brain-computer interfaces (BCI) and neural signal processing, with a focus on translating neural activity into practical assistive technologies. Her seminal 2010 work, "Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data," has garnered 132 citations and remains a foundational reference in the field. In this study, she systematically compared machine learning classifiers to decode motor imagery from EEG signals, demonstrating that simple linear models like LDA could achieve high accuracy for left-right limb movement classification. This work directly advanced the development of non-invasive BCI systems that enable disabled individuals to control external devices—such as computers or robotic prosthetics—through thought alone. Dr. Chatterjee’s contributions have significantly improved the accessibility and reliability of BCI technologies, enhancing quality of life for those with severe motor impairments. Her research continues to influence real-world applications in rehabilitation and human-computer interaction, making her a pivotal figure in the quest to bridge the gap between neural signals and seamless device control.

Research Focus

Key Achievements

Top Papers

1. 1
   Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data

   132 citations · 2010

Key Collaborators

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