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Abstract The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress dataset has been collected, and an explainable feature engineering (XFE) model has been proposed using the Directed Lobish (DLob) symbolic language. The first phase of this research is the data collection phase, and an EEG stress dataset was gathered from 310 participants. This collected stress dataset contains two classes: (i) stress and (ii) control. An XFE model has been presented to detect stress automatically. The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. The proposed XFE model generates a DLob string, and the explainable results were obtained using this string. Moreover, the presented XFE model attained 92.95% and 73.63% classification accuracy, deploying 10-fold and leave-one subject-out (LOSO) cross-validations (CV). According to the classification performances, the recommended QuadTPat-based XFE is a good model for EEG signal classification. Also, the presented QuadTPat-based XFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based XFE is cooperating with the DLob.

Abstract The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. This research uses a publicly available neonatal EEG signal dataset to get comparative results. In order to classify these EEG signals, an explainable feature engineering (EFE) model has been proposed. In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. By utilizing these levels, we have created an automaton and this automaton has three nodes (each node defines each level). In the feature extraction phase, transition tables of these nodes has been extracted. Therefore, the proposed feature extraction function is termed Triple Nodes Automaton-based Transition table Pattern (TATPat). The used EEG signal dataset contains 19 channels and there are 9 (= 32) connection in the defined automaton. Thus, the presented TATPat extracts 3249 (= 19 × 19 × 9) features from each EEG segment. To choose the most informative features of these 3249 features, a new feature selector which is CWNCA has been applied. By cooperating findings of this feature selector and the presented DLob, the explainable results have been obtained. The last phase is the classification phase and to get high classification performance from this phase, an ensemble classifier (tSVM) has been presented and the classification results have been obtained using two validation techniques which are 10-fold cross-validation (CV) and leave-one subject-out (LOSO) CV. The proposed EFE model generates a DLob string and by using this string, the explainable results have been obtained. Moreover, the presented EFE model attained 99.15% and 76.37% classification accuracy deploying 10-fold and LOSO CVs respectively. According to the classification performances, the recommended TATPat-based EFE is a good model at EEG signal classification. Also, the presented TATPat-based EFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based EFE is cooperating by the DLob.

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the 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 for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection. Materials and Methods: In this research, two distinct EEG signal datasets were used to obtain classification and explainable results. The recommended XFE model utilizes a channel-based minimum and maximum pattern (ChMinMaxPat) feature extraction function, which generates 15 distinct feature vectors from EEG data. Cumulative weight-based neighborhood component analysis (CWNCA) is employed to select the most informative features from these vectors. Classification is performed by applying an iterative and ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier to each feature vector. Information fusion is achieved through iterative majority voting (IMV), which consolidates the 15 tkNN classification results. Finally, the Directed Lobish (DLob) symbolic language generates interpretable outputs by leveraging the identities of the selected features. Together, the tkNN classifier, IMV-based information fusion, and DLob-based explainable feature extraction transform the model into a self-organizing explainable feature engineering (SOXFE) framework. Results: The ChMinMaxPat-based model achieved over 70% accuracy on both datasets with leave-one-record-out (LORO) cross-validation (CV) and over 90% accuracy with 10-fold CV. For each dataset, 15 DLob strings were generated, providing explainable outputs based on these symbolic representations. Conclusions: The ChMinMaxPat-based SOXFE model demonstrates high classification accuracy and interpretability in detecting violence and stress from EEG signals. This model contributes to both feature engineering and neuroscience by enabling explainable EEG classification, underscoring the potential importance of EEG analysis in clinical and forensic applications.

Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect the brain’s functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection.

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the 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 in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.

Tension-type headache (TTH) affects many individuals worldwide. Although the exact pathogenesis of TTH remains unclear, central, and peripheral mechanisms are considered to play a role in TTH 1. This pilot study aimed to investigate the role of transient receptor potential (TRP) channels in the development or chronic inflammation in TTH and to discuss the findings in the light of literature. This pilot study included a patient group comprising three patients with episodic TTH and three patients with chronic TTH (CTTH) aged 18-40 years with no comorbidities and a control group of three patients with no headache. Peripheral blood samples were obtained from all the participants, and both RNA and cDNA were isolated on the same day. The mRNA levels of pain-related TRP channels [TRPA1, TRP vanilloid-1 (TRPV1), TRPV3, TRPV4, TRPM3, and TRPM8] were measured by reverse transcriptase (RT)-quantititave polymerase chain reaction method and were normalized with the levels of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) transcript. Results were analyzed using statistical methods. All three groups were comparable with regard to demographic characteristics. No significant difference was found among the groups with regard to the mRNA levels of the TRP channels normalized by GAPDH, whereas the TRPM8 expression levels were not significantly lower in the CTTH group than in other groups (p=0.066). This study revealed that TRPM8 is likely to have a role in the pathogenesis of TTH, and this role of TRPM8 may be investigated by further studies.

Chronic alcoholism and acute ethanol poisoning have significant effects on the central nervous system. These effects can be divided into two groups. The first effect is the volume loss associated with a toxic effect on neurons mediated by neurotransmitters and/or receptors and electrolytes, and the secondary effects are associated with the sequela of liver cirrhosis such as hepatic encephalopathy and coagulopathies, and the problems in the gastrointestinal tract leading to impairment of vitamin uptake, ultimately leading to Wernicke-Korsakoff syndrome. Herein, we report a 68-year-old male patient who presented to the emergency room with confusion associated with acute ethanol poisoning on the basis of chronic alcoholism. A brain computed tomography (CT) scan revealed symmetrical hemorrhagic necrosis in bilateral frontal regions. In our case, vasculopathies and infectious etiology were excluded through cerebral and carotid vertebral CT angiography, examinations, and laboratory findings. Investigation of the cause of this atypical radiographic finding revealed that ethanol ingestion leads to increased risk for hypertension, as well as cerebral hemorrhage by resulting in coagulation dysfunction; it triggers P53- and neuron-specific enolase- mediated apoptosis and necrosis, thereby resulting in neuronal damage. Acute and chronic use of ethanol has specific effects on the frontal lobe of the brain. The findings are discussed in light of the relevant literature.

Acute hydrocephalus is a rare manifestation of posterior circulation strokes. A patient with cerebellar infarct and secondary obstructive hydrocephalus is described. Repeated CT showed severe third and lateral ventricular dilation suggestive for obstructive hydrocephalus. Following the shunt placement, he recovered and walks without assistance