Arama Sonuçları
Listeleniyor 1 - 7 / 7
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 Neuroimaging findings related to panic disorder: a brief review(Klinik Psikoloji Araştırmaları Derneği, 2022-12-26) Kazancı, Dilara; Saltoğlu, Seren; Erdoğdu, EmelPanic disorder (PD) is defined by recurrent unanticipated panic attacks and anxiety of losing control, which negatively affects the patients’ quality of life. Various neuroimaging techniques allow to assess brain structure or function and therefore represent important tools to understand the mechanisms related to PD pathology. Current studies have highlighted neural differences between PD patients and healthy controls using MRI, PET, SPECT, or EEG. However, there is an urgent need to discuss findings from various investigations simultaneously in order to obtain a multidimensional understanding of PD pathology, which further allows identifying possible target regions for more effective treatment or prevention strategies. Therefore, the present work briefly reviewed PD related neuroimaging studies published between 2012 and 2021. Relevant articles were searched using a combination of keywords relevant to various neuroimaging techniques (e.g., MRI, MRS, PET, EEG, fNIRS) and to PD (e.g., panic, anxiety, panic disorder). Studies involving patients with comorbid conditions other than agoraphobia and participants aged under 18 were excluded. A total of 20 studies fulfilling inclusion criteria were considered in this review. Most of the reviewed studies point to structural and functional neural changes in regions of the proposed fear network mostly including the hippocampi, thalamic nuclei, amygdala, anterior cingulate corti, insulae and other frontal lobe regions. Such neural changes in PD are thought to result in a hypersensitive fear network affecting normal emotional processing. Finally, studies showed that different treatments can partly reverse these changes, which significantly improves the quality of life in PD patients.Yayın Cognitive decision investigation with combined EEG gamma oscillations and eyetracking (EOG)(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2024-09-13) Sara, Ayça Burçak; Demirer, Rüştü Murat; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Electronics Engineering M.S. ProgramWith the progress in technology, the integration of neuroscience and sensory has assumed a very crucial role in discovering or unraveling cognitive functions. This thesis explores how EEG gamma oscillations and EOG eye tracking signals relate to cognitive decisions. Cognitive decision making, which encompasses attention, memory, and problem-solving abilities, is inherent in human functioning and hence grasping cognition enhances several fields including psychology, neuroscience, decision-making theories, among others. The study builds on the EEGEyeNet dataset to understand the coupling between the EEG activity and eye movements in decision making tasks, for 356 subjects in total. The contribution of high frequency gamma oscillations to saccadic eye movements is therefore investigated using additional EEG and EOG analysis techniques such as CPSD and phase space analysis. The findings clearly show that at a lower frequency band, the results are partially in line with the assumption that EEG and EOG activity is partially coordinated to the extent of decision-making task, while at high frequency bands, the EEG and EOG signals are partially asynchronous. This could suggest that this independence is the result of dissociated cognitive and neural substrates that control brain function and eye motion during processes that involve decision-making. The overall conclusions of this thesis help to advance the knowledge of how EEG and EOG signals can be combined for the investigation of cognitive decision-making processes. The study equally provides evidence towards the pliability and possibility of integrating these together for analysis of the neurological drivers of decision making while at the same time creating further research suggestions grounded on the accomplishment of achieving a blend of multiple physiological data feeds. Which could in turn bring about better cognitive models and better methods of rectifying poor decision making. It is hoped that this study will serve as a useful resource for other scholars from cognitive psychology, neuroscience, and human computer interaction where it may help advance studies on brain processes related to eye movement and decision making.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).Yayın Boundary element method for EEG single-dipole localization: a study in patients with OCD(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Abdullahi, Fatima I.; Demirer, Rüştü MuratThis study investigates EEG dipole localization in patients diagnosed with obsessive-compulsive disorder (OCD) using the Boundary Element Method (BEM) implemented via Brainstorm and OpenMEEG. EEG signals from 33 OCD patients were analyzed using a realistic, multi-layer head model consisting of scalp, skull, and brain tissues with respective conductivity values. Dipoles were accurately localized for each discrete time instant within the gamma frequency range (20-50 Hz) using a single dipole assumption per time point. EEG potentials measured from 19 standard electrodes were numerically computed by solving the forward EEG problem with the boundary element approach provided by OpenMEEG. Spectral clustering analysis identified distinct neural patterns corresponding to clinically recognized OCD subtypes, facilitating better diagnostic interpretations. Our results address previous methodological limitations by combining realistic head geometry modeling and precise temporal and spatial dipole estimation, offering promising directions for enhanced EEG-based diagnostic tools in psychiatry.Yayın Theta and Beta1 frequency band values predict dyslexia classification(John Wiley and Sons Ltd, 2025-12-29) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia, impacting children's reading skills, prompts families to seek cost-effective neurofeedback therapy solutions. Utilising machine learning, we identified predictive factors for dyslexia classification. Employing advanced techniques, we gathered 14-channel Quantitative Electroencephalography (QEEG) data from 200 participants, achieving 99.6% dyslexic classification accuracy through cross-validation. During validation, 48% of dyslexic children's sessions were consistently classified as normal, with a 95% confidence interval of 47.31 to 48.68. Focusing on individuals consistently diagnosed with dyslexia during therapy, we found that dyslexic individuals exhibited higher theta values and lower beta1 values compared to typically developing children. This study pioneers machine learning in predicting dyslexia classification factors, offering valuable insights for families considering neurofeedback therapy investment.
