arXiv subjects

Okba Bekhelifi

Publications and source records attributed to Okba Bekhelifi.

3 recordsLinked to original sources

arXiv2026

Optimized Embedded Implementation of Hyperspectral-Multispectral Image Fusion on Raspberry Pi

Remote sensing optical images have become central to a wide range of applications. In particular, hyperspectral images, with their high spectral resolution, enable the extraction of rich information about the objects and materials present in the observed scene. Nevertheless, processing such data comes at the expense of a high computational load due to its large data volume, making real-time processing very difficult to achieve. Recently, we proposed an approach to investigate the feasibility of processing such data on a Raspberry Pi by implementing a hyperspectral super-resolution technique, namely HSB-SV. However, the implementation resulted in high computational time. To overcome this limitation, we apply computational optimization techniques based on migrating the most intensive operations to PyTorch and edge inference frameworks such as ONNX Runtime and ExecuTorch with the XNNPACK backend. The proposed optimized implementation is deployed on a Raspberry Pi 5 platform. Experimental results demonstrate a significant reduction in computational time, achieving a 1.48x overall speedup on the Raspberry Pi 5, the total execution time decreases significantly, from 527.1 ms to 356.7 ms corresponding to a 32.3% dminution, while fully preserving the fusion quality, making the approach more suitable for embedded and edge computing scenarios, particularly for UAV-based hyperspectral remote sensing applications.

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arXiv2026

Which Metric Reflects the Spelling Rate Accuracy in Event-Related Potential-Based Brain-Computer Interfaces?

For predictive models, the often-reported performance metrics are the loss and accuracy. In synchronous Brain- Computer Interface (BCI) systems, these metrics are informative for most BCI paradigms; however, for Event-Related Potential (ERP) applications the spelling rate, which measures the number of characters correctly selected is more important as it influences the estimation of information transfer rate (ITR) and any related metric measuring spelling performance. Moreover, ERP-based BCIs hold imbalanced data class distributions, which require reporting metrics that can handle the imbalance, such as the area under the receiver operating characteristic curve (ROC AUC). In this work, we study the correlation of the spelling rate with 13 metrics to identify which among them best reflect user spelling performance and how they are affected by trial repetition. The Results of two datasets (a private LARESI ERP dataset and the public OpenBMI ERP dataset) favor the Brier score, Matthews Correlation Coefficient (MCC), and the metrics that account for class imbalance in binary classification: ROC AUC, area under the Precision-Recall curve (PR AUC), Average Precision (AP), and partial AUC (pAUC). These findings encourage researchers and practitioners to report those metrics in ERP-based BCI experiments.

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arXiv2022

Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study

Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked potentials remains challenging and imposes hard constraints for signal processing and machine learning techniques. Recent advances in stimulus presentation and deep learning showcased a promising direction in significantly improving the efficacy of those systems, in this study we propose the combination of colored inverted face stimulation with classification using convolutional neural networks in the hard settings of dry electrodes and fast flashing single-trial ERP-based BCI. The high online accuracy achieved, with two subjects passing the 90 percent correct symbol detection bar and a transfer rate above 60 bits per minute, demonstrates the approach potential in improving the practicality of ERP based BCIs.

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