Anwesha Khasnobish

About

Anwesha Khasnobish is a prominent researcher specializing in Brain-Computer Interfaces (BCI), EEG signal processing, and human-robot interaction, with significant contributions to assistive and rehabilitation technologies. Her foundational work on classifying limb movement patterns from EEG signals has been particularly influential — her 2010 study analyzing LDA, QDA, and KNN classification algorithms has garnered 132 citations, establishing her as a key voice in BCI methodology. Building on this, her 2011 investigation into left/right hand movement classification further solidified her expertise, attracting 74 citations and advancing the field's understanding of intelligent signal interpretation. Khasnobish's research extends beyond signal classification into practical applications, including EEG-controlled remote robotic systems and motor imagery-driven robot navigation — work that bridges neuroscience and engineering in meaningful ways for disabled populations. Her later research explores more nuanced territories, including Interval Type-2 Fuzzy Sets for complex movement execution and vibrotactile feedback systems, enabling humans to perceive robotic sensory information more intuitively. Across her body of work, Khasnobish consistently pursues a central humanitarian vision: leveraging neural signals and intelligent algorithms to restore autonomy and improve quality of life for individuals with physical disabilities.

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
2. 2
   Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms

   74 citations · 2011
3. 3
   A Two-fold classification for composite decision about localized arm movement from EEG by SVM and QDA techniques

   28 citations · 2011
4. 4
   Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

   27 citations · 2016
5. 5
   EEG controlled remote robotic system from motor imagery classification

   16 citations · 2012
6. 6
   Implementation of EEG based control of remote robotic systems

   14 citations · 2011
7. 7
   Vibrotactile feedback for conveying object shape information as perceived by artificial sensing of robotic arm

   9 citations · 2016
8. 8
   Texture- and deformability-based surface recognition by tactile image analysis

   8 citations · 2016
9. 9
   Interfacing robotic tactile sensation with human vibrotactile perception for digit recognition

   7 citations · 2014

Key Collaborators

Contact & Links