Manali Saini
Papers
1
Total Citations
8
H-Index
1
About
Manali Saini is a rising researcher in neural engineering and human–machine interaction, with a primary focus on decoding movement intent from early brain signals. Her work centers on kinematic parameter estimation from pre-movement electroencephalography (EEG), a critical step toward seamless control of wearable robotic systems. In her landmark study, “BiCurNet,” she developed a novel neural decoder that estimates biceps curl trajectory from EEG signals recorded before visible movement—addressing a significant gap in surface EEG-based early decoding. This work, already garnering 8 citations soon after publication in 2023, demonstrates her ability to tackle challenging, under-explored problems in brain–computer interfaces. By integrating simultaneous EEG and kinematic data, Saini’s research paves the way for more intuitive, responsive assistive devices that can anticipate user intent. Her contributions are particularly valuable for advancing positive augmentation in rehabilitation and prosthetics, where early detection of movement parameters can dramatically improve user experience and device performance. As an emerging scholar, Manali Saini is establishing herself at the forefront of non-invasive neural decoding for real-world motor augmentation.
Research Focus
Key Achievements
Top Papers
- 1