A5057878838- EEG and Brain-Computer Interfaces
- Epilepsy research and treatment
- Trigeminal Neuralgia and Treatments
- Parkinson's Disease Mechanisms and Treatments
- Migraine and Headache Studies
- Long-Term Effects of COVID-19
- Functional Brain Connectivity Studies
- Multiple Sclerosis Research Studies
- Neurological disorders and treatments
- Emotion and Mood Recognition
- ECG Monitoring and Analysis
- Biochemical Acid Research Studies
- Heart Rate Variability and Autonomic Control
- Autoimmune and Inflammatory Disorders Research
- Polyomavirus and related diseases
- Blind Source Separation Techniques
- Image Processing Techniques and Applications
- Infectious Encephalopathies and Encephalitis
- Neuroscience and Neuropharmacology Research
- Ocular Diseases and Behçet’s Syndrome
- Biochemical effects in animals
- Neural and Behavioral Psychology Studies
- Ophthalmology and Eye Disorders
- Peripheral Neuropathies and Disorders
- Alcoholism and Thiamine Deficiency
Fırat University
2014-2025
Inonu University
2024
Turgut Özal University
2018-2022
Malatya Turgut Özal Üniversitesi
2022
Malatya Devlet Hastanesi
2017-2022
Manisa Celal Bayar University
2021
Koç University
2021
Mardin Artuklu University
2018
Background: Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. In this context, we aimed to investigate cortical activities of psychotic criminal subjects by deploying an explainable feature engineering (XFE) model using EEG dataset. Methods: study, a new dataset was curated, containing signals from and control groups. To extract meaningful findings dataset, presented channel-based extraction function named...
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented literature. We developed computationally lightweight machine model diagnosis novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain images were prospectively acquired from...
Background\Objectives: Solving the secrets of brain is a significant challenge for researchers. This work aims to contribute this area by presenting new explainable feature engineering (XFE) architecture designed obtain results related stress and mental performance using electroencephalography (EEG) signals. Materials Methods: Two EEG datasets were collected detect stress. To achieve classification results, XFE model was developed, incorporating novel extraction function called Cubic Pattern...
Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect functioning. However, manual interpretation of EEG is tedious and time-consuming. Therefore, automatic translation models need to be proposed using machine learning methods. In this study, we an innovative method achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), t algorithm, Lobish (a symbolic language). By...
This paper presents a novel, explainable feature engineering framework for classifying EEG and ECG signals with high accuracy. The proposed method employs the Order Transition Pattern (OTPat) extractor. presented OTPat extractor captures both channel/column-based patterns (spatial features) using all channels each point signal/row-based (temporal by extracting features from individual overlapping blocks. extracted are then refined cumulative weighted iterative neighborhood component analysis...
Abstract Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In work, we developed a novel function, black-white hole pattern (BWHPat) which dynamically selects most suitable from 14 options. We BWHPat in four-phase engineering model, involving multileveled extraction, selection, classification, cortex map generation. Textural statistical...
The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing very important for neuroscience and machine learning (ML). primary objective of this research detect neonatal seizures explain these using new version Directed Lobish. This uses a publicly available dataset get comparative results. In order classify signals, an explainable feature engineering (EFE) model has been proposed. EFE...
The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing crucial for neuroscience and machine learning (ML). a new stress dataset has been collected, an explainable feature engineering (XFE) model proposed using Directed Lobish (DLob) symbolic language. first phase of this research phase, was gathered from 310 participants. This collected contains two classes: (i) (ii) control. An...
Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of brain, are one most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data violence detection. The primary objective is assess classification capability proposed XFE model, which uses next-generation extractor, obtain interpretable findings EEG-based stress Materials Methods:...
Elektroensefalogram (EEG), beyindeki elektriksel aktivitenin izlenmesi için yaygın olarak kullanılmaktadır. EEG sinyallerinin hekimler tarafından incelenmesi yorucu ve zaman alıcıdır. Bu nedenle, algılama doğruluğunu artırmak makine öğrenme teknikleri kullanılabilir. çalışmada 35 kanal, 10575x15 saniye normal 11240x15 anormal sinyalinden oluşan 2 sınıflı veri seti oluşturulmuştur. very Turgut Özal Üniversitesi Malatya Eğitim Araştırma Hastanesi’ ne 2021 yılında başvuran hastaların sinyalleri...
Diabetic neuropathy is the most frequent chronic complication of diabetes. It may attack to sensory, motor or autonomous fibers. Varied mechanisms account for development diabetic such as metabolic disorders, microvascular damages, neurotrophic support deficit, alternation in neuro-immune interactions, neural and glial cell apoptosis, inflammation. Alpha lipoic acid (ALA) a potent lipophilic antioxidant vitro vivo conditions, which plays main role cofactor many mitochondrial reactions,...
ABSTRACT Background: During the pandemic, many neurological symptoms have been evaluated as complications of COVID-19 pneumonia. Objective: To investigate frequency and characteristics findings, their effects on prognosis patients with pneumonia who consulted Neurology department. Methods: Data 2329 were hospitalized diagnosis in our hospital scanned. The clinical, laboratory radiological findings relating to treatment 154 required consultation retrospectively by reviewing clinical notes....
Neuroleptic malignant syndrome is characterized by muscle stiffness, hyperthermia, autonomic dysfunction, elevation in serum creatine phosphokinase, and changes consciousness, which usually occur due to the side effects of life-threatening neuroleptic antipsychotic drugs, it can cause high mortality. A few cases associated with coronavirus disease 2019 infection vaccination have been reported literature. Our case presented epileptic seizure signs 10 days after receiving a single dose...