3 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 3 / 3
Yayın A comparison of Auto Train Brain neurofeedback rewarding interfaces in terms of efficacy(Acıbadem Mehmet Ali Aydınlar Üniversitesi, 2023-01-01) Eroğlu, GünetBackground/aim: Auto Train Brain is a mobile app that was specifically developed for dyslexic children to increase their reading speed and reading comprehension. In the original mobile app, only one unique neurofeedback user interface provided visually and audibly rewarding feedback to the subject with a red-green colored arrow on the screen. Later, new modules are added to the app with the end-users requests. These are the “youtube” video-based interface and “Spotify” auditory-based interface. In this research, we have compared the efficacy of the neurofeedback rewarding interfaces. Materials and methods: The experiment group consists of 20 dyslexic children aged 7-to 10 (15 males, 5 females) who were randomly assigned to one rewarding interface and used it at home for more than six months. Results: The result indicates that though the “youtube” interface is liked most by the participants, the arrow-based simple neurofeedback interface reduces theta brain waves more than other rewarding schemes. On the other hand, “youtube” and “Spotify” based interfaces increase Beta band powers more than the arrow interfaces in the cortex. The ”Spotify” user interface improves the fast brain waves more on the temporal lobes (T7 and T8) as the feedback given was only auditory. Conclusion: The results indicate that the relevant neurofeedback rewarding interface should be chosen based on the dyslexic child’s specific condition.Yayın Auto Train Brain increases the variance of the gamma band sample entropy in the left hemisphere in dyslexia: a pilot study(Springer Science and Business Media Deutschland GmbH, 2023) Eroğlu, GünetAuto Train Brain is a mobile app that improves reading speed and reading comprehension in dyslexia. The efficacy of Auto Train Brain was proven with a clinical trial. We have analyzed the long-term training effects of the Auto Train Brain on dyslexic children. We have collected QEEG data from 14 channels from 21 dyslexic children for 100 sessions and calculated the sample entropy in the gamma bands for the left posterior brain (T7, P7, and O1). Although the gamma band values fluctuate and no permanent increase in the gamma band values is detected after Auto Train Brain training at T7, P7, and O1, the variance of gamma band sample entropy increases as the neurofeedback session number increases. We have concluded that the Auto Train Brain increases the flexibility of the left brain in dyslexia.Yayın Secure and interpretable dyslexia detection using homomorphic encryption and SHAP-based explanations(Institute of Electrical and Electronics Engineers Inc., 2025-10-25) Harb, Mhd Raja Abou; Çeliktaş, Barış; Eroğlu, GünetProtecting sensitive healthcare data during machine learning inference is critical, particularly in cloud-based environments. This study addresses the privacy and interpretability challenges in dyslexia detection using Quantitative EEG (QEEG) data. We propose a privacy-preserving framework utilizing Homomorphic Encryption (HE) to securely perform inference with an Artificial Neural Network (ANN). Due to the incompatibility of non-linear activation functions with encrypted arithmetic, we employ a dedicated approximation strategy. To ensure model interpretability without compromising privacy, SHapley Additive exPlanations (SHAP) are computed homomorphically and decrypted client-side. Experimental evaluations demonstrate that the encrypted inference achieves an accuracy of 90.03% and an AUC of 0.8218, reflecting only minor performance degradation compared to plaintext inference. SHAP value comparisons (Spearman correlation = 0.59) validate the reliability of the encrypted explanations. These results confirm that integrating privacy-preserving and explainable AI approaches is feasible for secure, ethical, and compliant healthcare deployments.
