3 citations · 3 across the 5 of their papers we have counts for
9 papers
ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification
Abdul Basit, Saim Rehman, Muhammad Shafique
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-…
EEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding
Abdul Basit, Saim Rehman, Muhammad Shafique
Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder can become a…
AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders
Saim Rehman, Muhammad Shafique
Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI C…
GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS
Saim Rehman, Muhammad Shafique
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 guar…
BRUCE: Benchmarking Robustness Under Corruption Escalation for Scientific Vision-Language Reasoning
Saim Rehman, Muhammad Shafique
Visual-language models (VLMs) frequently struggle with robustness issues in real-world situations due to low- or varying-quality input images. In this paper, we aim at analyzing VL…
QSTAR: Quantum Selective Transfer with Adaptive Routing
Saim Rehman, Nouhaila Innan, Muhammad Shafique
Quantum transfer learning (QTL) is often evaluated by replacing a classical classifier with a fixed variational quantum head, but this hides a key question: when is the quantum bra…
Towards Fair Benchmarking of Quantum Transfer Learning for Visual Classification
Nouhaila Innan, Saim Rehman, Muhammad Shafique
Quantum Transfer Learning (QTL) offers a promising approach for visual quantum machine learning under near-term constraints, where limited qubit counts, shallow circuit depths, and…
Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
Abdul Basit, Saim Rehman, Muhammad Shafique
Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with vary…
CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman +1
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action pr…