44 citations · 61 across the 8 of their papers we have counts for
8 papers
From Dyad to Triad: Eliciting XAI Requirements in Stroke Rehabilitation
Param Rajpura, Yogesh Kumar Meena
Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfac…
SkeySpot: Automating Service Key Detection for Digital Electrical Layout Plans in the Construction Industry
Dhruv Dosi, Rohit Meena, Param Rajpura +1
Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. Ho…
Can EEG resting state data benefit data-driven approaches for motor-imagery decoding?
Rishan Mehta, Param Rajpura, Yogesh Kumar Meena
Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data…
Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena
Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are…
Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge
Param Rajpura, Yogesh Kumar Meena
Decoding EEG during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning me…
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena
This review paper provides an integrated perspective of Explainable Artificial Intelligence techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret…
Dataset Augmentation with Synthetic Images Improves Semantic Segmentation
Manik Goyal, Param Rajpura, Hristo Bojinov +1
Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts requi…
Object Detection Using Deep CNNs Trained on Synthetic Images
Param S. Rajpura, Hristo Bojinov, Ravi S. Hegde
The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications.…