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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. [ABSTRACT FROM AUTHOR]

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.

Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts. A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm. The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish. The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language. [ABSTRACT FROM AUTHOR]