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Yayın EEG signal compression based on classified signature and envelope vector sets(Wiley, 2009-03) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs an effective k-means clustering algorithm. It is assumed that both the transmitter and the receiver units have the same CSEVS. In this work, on a frame basis, EEG signals are modeled by multiplying only three factors called as classified signature vector, classified envelope vector, and gain coefficient (GC), respectively. In other words, every frame of an EEG signal is represented by two indices R and K of CSEVS and the GC. EEG signals are reconstructed frame by frame using these numbers in the receiver unit by employing the CSEVS. The proposed method is evaluated by using some evaluation metrics that are commonly used in this area such as root-mean-square error, percentage root-mean-square difference, and measuring with visual inspection. The performance of the proposed method is also compared with the other methods. It is observed that the proposed method achieves high compression ratios with low-level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.Yayın k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia(Taylor & Francis, 2023) Eroğlu, Günet; Arman, FehimLearning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.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).
