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
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
Post-training quantization of vision--language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving a headline score does not guarantee preservation of visual grounding behavior. We present GHOST-Q, a cross-precision controlled evaluation of three 8B VLM families under...
This paper proposes BRUCE (Benchmarking Robustness Under Corruption Escalation), a multimodal reasoning fragility framework for scientific vision-language reasoning that evaluates VLMs'robustness by applying perturbations and distortions to the input images, such as blur or low contrast.
S. Rehman, Muhammad Shafique· 0 citations
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