Saurabh Prasad

About

Saurabh Prasad is a leading researcher in non-invasive brain-machine interfaces (BMI), with a particular focus on decoding lower-limb movement intentions from electroencephalography (EEG) to restore mobility. His seminal 2013 work on high-accuracy decoding of user intentions to control a lower-body exoskeleton (191 citations) demonstrated that scalp-recorded EEG could reliably drive assistive devices, offering a non-invasive alternative to surgical implants. Expanding on this, his 2014 study (130 citations) showed that movement-related cortical potentials (MRPs) can be decoded *before* movement execution, enabling real-time, predictive control for standing and sitting transitions. Prasad also pioneered advanced signal processing techniques, including locality-preserving dimensionality reduction, to classify sit-to-stand and stand-to-sit transitions from low-frequency EEG. His work bridges fundamental neuroscience and practical rehabilitation engineering, proving that non-invasive BMIs can achieve the precision needed for real-world exoskeleton control. With a cumulative citation impact exceeding 330 for these core papers, Prasad’s contributions are pivotal for developing accessible, thought-controlled mobility aids for individuals with paralysis.

Research Focus

Key Achievements

Top Papers

1. 1
   High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton

   191 citations · 2013
2. 2
   Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution

   130 citations · 2014
3. 3
   Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction

   10 citations · 2013

Key Collaborators

Contact & Links