Pallavi Kaushik
Papers
1
Total Citations
10
H-Index
1
About
Pallavi Kaushik is a leading researcher at the intersection of brain-computer interfaces (BCIs) and deep learning, with a primary focus on motor activity recognition from EEG signals. Her most impactful work, published in 2023, introduces a novel ensemble approach combining Stacked Bidirectional LSTM-LSTM networks with a Transformer model to decode real-life motor intentions from electroencephalography data. This research, which has already garnered 10 citations, addresses a critical challenge in BCI technology: accurately predicting complex motor activities in naturalistic settings. By fusing the temporal modeling strengths of recurrent architectures with the attention mechanisms of Transformers, Kaushik’s work pushes the boundaries of non-invasive neural decoding, offering promising pathways for prosthetic control and rehabilitation technologies. Her contributions stand out for their practical orientation toward real-world applications, moving beyond controlled laboratory conditions. As an emerging voice in computational neuroscience, Kaushik’s innovative hybrid modeling strategies are shaping next-generation BCI systems, demonstrating how deep learning can bridge the gap between raw neural data and meaningful motor control.
Research Focus
Key Achievements
Top Papers
- 1