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    BCI for mobile devices: Time-frequency and representation learning analysis of mobile gesture tasks
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yılmaz, Çagatay Murat; Ulu, Ahmet; Demirbaş, Gülşah
    Mobile devices are central to daily life, yet their interaction remains primarily limited to touch and voice, which may be impractical in restricted mobility scenarios. Brain-computer interfaces (BCIs) offer a promising alternative by enabling device control through neural activity. Although motor imagery (MI) BCIs have been extensively studied, MI signals for mobile-specific gestures remain relatively underexplored. This study addresses this gap by investigating the effectiveness of combining time-frequency transformations with deep learning for classifying MI of mobile gestures. Using the MI-BMPI dataset-comprising EEG recordings of participants imagining tapping and swiping, and among the few publicly available datasets of its kind-we applied Short Time Fourier Transform, Continuous Wavelet Transform, Stockwell Transform, and Hilbert-Huang Transform as inputs to architectures including ResNeSt-50d, HRNet-W18, ConvNeXt-Base, DeiT-Base, Swin Transformer-Tiny, and RegNetX-002. In the classification of tapping and swiping MI tasks, performance across different subjects ranged from 0.61 to 0.947 when averaged over five random seeds, while individual subject models achieved results between 0.708 and 0.975. These promising outcomes are consistent with other evaluation metrics. Overall, this work provides the first systematic evaluation of mobile gesture MI-BCIs, demonstrating that time-frequency representations coupled with deep learning enable robust EEG-based mobile interaction and laying the groundwork for the development of future neuroadaptive technologies.
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    Region-specific topographic representations for deep learning-based brain-computer interfaces
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yılmaz, Çağatay Murat; Ulu, Ahmet
    Electroencephalography (EEG) signals are widely used across disciplines and are promising for future medical and technological applications. However, current EEG analysis methods often fall short in classification accuracy, limiting progress in brain-computer interfaces (BCIs), early diagnosis of neurological disorders, and AI-driven health technologies. This paper proposes a region-specific spatial-spectral topographic mapping approach to enhance EEG-based classification using deep learning. The core objective is to improve discriminative feature learning by focusing only on brain regions relevant to motor-related activity and utilizing spectral representations (alpha, beta, and alpha/beta ratio). Pre-trained deep learning architectures-ResNeSt-50d, HRNet-W18, and ConvNeXt-Base-were fine-tuned on topographic images generated from the BCI Competition IV dataset 2a for classifying left- and right-hand motor imagery tasks. The proposed method achieved classification accuracies exceeding 75% when data from multiple sessions were aggregated. These results demonstrate the effectiveness of spatially and spectrally informed topographic representations for robust EEG-based BCI systems and lay a foundation for integrating EEG signals into advanced artificial intelligence applications.
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    A survey on post-quantum based approaches for edge computing security
    (John Wiley and Sons Inc, 2024) Karakaya, Aykut; Ulu, Ahmet
    With the development of technology and its integration with scientific realities, computer systems continue to evolve as infrastructure. One of the most important obstacles in front of quantum computers with high-speed processing is that its existing systems cause security vulnerabilities. Therefore, in order to take advantage of quantum systems, existing systems that are already secure must also be secure in the post-quantum scenario. One of these systems is edge computing. There are challenges in terms of computational power for the implementation of pre- and post-quantum methods in structures with resource-constrained devices. This article reviews the post-quantum security threats of edge devices and systems and the secure methods developed for them. Although there is relatively little research in this field, it remains relevant. In the studies reviewed, lattice-based approaches are often highlighted for making edge systems quantum-resistant. Additionally, these studies indicate that there has been an increasing trend in this field in recent years. This article is categorized under: Applications of Computational Statistics > Defense and National Security Algorithms and Computational Methods > Networks and Security.