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Yayın Electroencephalography signatures associated with developmental dyslexia identified using principal component analysis(Multidisciplinary Digital Publishing Institute (MDPI), 2025-08-27) Eroğlu, Günet; Harb, Mhd Raja AbouBackground/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensionality reduction and clustering techniques to high-density electroencephalographic (EEG) recordings. We further examined the functional relevance of these features to reading performance under standardised test conditions. Methods: EEG data were collected from 200 children (100 with dyslexia and 100 age- and IQ-matched typically developing controls). Principal Component Analysis (PCA) was applied to high-dimensional EEG spectral power datasets to extract latent neurophysiological components. Twelve principal components, collectively accounting for 84.2% of the variance, were retained. K-means clustering was performed on the PCA-derived components to classify participants. Group differences in spectral power were evaluated, and correlations between principal component scores and reading fluency, measured by the TILLS Reading Fluency Subtest, were computed. Results: K-means clustering trained on PCA-derived features achieved a classification accuracy of 89.5% (silhouette coefficient = 0.67). Dyslexic participants exhibited significantly higher right parietal–occipital alpha (P8) power compared to controls (mean = 3.77 ± 0.61 vs. 2.74 ± 0.56; p < 0.001). Within the dyslexic group, PC1 scores were strongly negatively correlated with reading fluency (r = −0.61, p < 0.001), underscoring the functional relevance of EEG-derived components to behavioural reading performance. Conclusions: PCA-derived EEG patterns can distinguish between dyslexic and typically developing children with high accuracy, revealing spectral power differences consistent with atypical hemispheric specialisation. These results suggest that EEG-derived neurophysiological features hold promise for early dyslexia screening. However, before EEG can be firmly established as a reliable molecular biomarker, further multimodal research integrating EEG with immunological, neurochemical, and genetic measures is warranted.Yayın ANN activation function estimators for homomorphic encrypted inference(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, BarışHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.Yayın Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, FarshadRecent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.Yayın “Can we use a biomarker detection algorithm to measure the effectiveness of 14-channel neurofeedback in dyslexia?”(Routledge, 2025-10-01) Eroğlu, Günet; Harb, Raja AbouDyslexia, one of children’s most common neurological diversities, primarily manifests as a reduced reading ability. Genetic factors contribute to dyslexia, with contemporary theories attributing it to a delay in left hemispheric lateralization that reduces effective reading and writing skills. To assist dyslexic children, smartphone application, Auto Train Brain, has been developed to enhance reading comprehension and speed. Previously, the efficacy of the mobile application’s training program was assessed using psychometric tests; however, our study employed a biomarker detection software to evaluate the neurofeedback’s impact. Machine learning (ML) techniques have recently gained traction in differentiating between dyslexia and typically developing children (TDC). The dataset of this study consists of 100 sessions of 2-minute resting-state eyes-open 14-channel Quantitative Electroencephalography (QEEG) data from 100 children with dyslexia and 100 TDC. Therefore, the dyslexia biomarker detection software assessed the efficacy of the 14-channel neurofeedback administered via Auto Train Brain. Results showed significant improvement in electrophysiological normalization, increasing from 30% in the first 20 sessions to 61% by the end of the training. A two-proportion Z-test confirmed this improvement was statistically significant (Z = −3.96, p = 0.00007), particularly between the 1–20 and 1–60 session intervals (Z = −2.66, p = 0.0079).
