Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple sclerosis MRI (MS-MRI) provides a high-stakes textual-knowledge test case because correct reasoning requires current diagnostic criteria, standardized acquisition and repo...
Abdul Basit, M. Hanif, Muhammad Shafique· 0 citations
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preproce...
Abdul Basit, S. Rehman, Muhammad Shafique· 0 citations
EEG-Fusion is presented, a failure-informed decision-level fusion framework that treats source-free MI decoding as label-free reliability estimation over heterogeneous experts and suggests that label-free reliability estimation can reduce subject-level failure modes in source-free MI-EEG deployment.
Abdul Basit, S. Rehman, Muhammad Shafique· 0 citations
Supporting fine motor interaction with upper-limb prostheses demands an interface intended to be intuitive, low-latency, and robust to user and signal variability. This challenge is particularly acute when EMG signals are unreliable, as in conditions such as ALS or high cervical SCI, where EEG-based BCIs offer a potent...
Abdul Basit, Azaz-Ur-Rehman Nasir, Mathew Obsequio Ponon et al.· IEEE Access· 0 citations
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