ArticleDiagnostics (Basel, Switzerland)2024
Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Differential quadruple pattern: A new EEG signal classification framework.IBRO neuroscience reports · 2026Article
- Different pattern: a new EEG-based method for mental performance detection.BMC medical informatics and decision making · 2026Article
- DiagPat: An Explainable Language Detection Model Using EEG Signals.Sensors (Basel, Switzerland) · 2026Article
- DMPat-based SOXFE: investigations of the violence detection using EEG signals.Cognitive neurodynamics · 2025Article
- ThinkSTra: a transformer-driven architecture for decoding imagined speech from EEG with spatial-temporal dynamics.Medical & biological engineering & computing · 2025Article
- An explainable EEG epilepsy detection model using friend pattern.Scientific reports · 2025Article
- Novel accurate classification system developed using order transition pattern feature engineering technique with physiological signals.Scientific reports · 2025Article
- CubicPat: Investigations on the Mental Performance and Stress Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Minimum and Maximum Pattern-Based Self-Organized Feature Engineering: Fibromyalgia Detection Using Electrocardiogram Signals.Diagnostics (Basel, Switzerland) · 2024Article
- ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals.Diagnostics (Basel, Switzerland) · 2024Article
- QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals.Scientific reports · 2024Article
- TATPat based explainable EEG model for neonatal seizure detection.Scientific reports · 2024Article
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Authors and funding
6 authors.
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Abstract
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.
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