14 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…
EEGForceFusion: Joint Tokenised-Continuous Representation Learning for Subject-Independent Grasp Force Decoding
Sankalp Sunil Turankar, Yogesh Kumar Meena
Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invas…
Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging
Parth G. Dangi, Yogesh Kumar Meena, Yogesh Kumaar Meena
Brain-machine interfaces (BMIs) can assist individuals with limited mobility, such as stroke survivors or amputees. One of the key challenges in developing BMIs is expanding their…
SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
Gourav Siddhad, Yogesh Kumar Meena
Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically e…
RL-ACRGNet: Reinforcement Learning-Based Chest Radiology Report Generation Network
Yogesh Kumar Meena, Saurabh Agarwal, K. V. Arya
Medical imaging interpretation is a foundational pillar of modern clinical diagnostics, yet the manual generation of radiology reports remains a time-consuming process prone to int…
A Domain-Informed Multi-Objective Framework for EEG Channel Selection in Motor Imagery BCIs
Dekka Muni Kumar, Dhruba Jyoti Kalita, Yogesh Kumar Meena
Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional EEG channel selection methods…