Bektas Omer, Kirik Serkan, Tasci Irem, Hajiyeva Rena, Aydemir Emrah, Dogan Sengul, Tuncer Turker
Department of Pediatrics, Division of Pediatric Neurology, Faculty of Medicine, Ankara University, Ankara 06100, Turkey.
Department of Pediatrics, Division of Pediatric Neurology, Fethi Sekin City Hospital, Elazig 23280, Turkey.
Diagnostics (Basel). 2024 Nov 26;14(23):2666. doi: 10.3390/diagnostics14232666.
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.
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.
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.
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.
脑电图（EEG）信号常被称为大脑的字母，是收集有关大脑活动的有价值信息的最具成本效益的方法之一。本研究提出了一种新的可解释特征工程（XFE）模型，旨在对用于暴力检测的EEG数据进行分类。主要目的是评估所提出的XFE模型的分类能力，该模型使用了下一代特征提取器，并获得基于EEG的暴力和压力检测的可解释结果。
在本研究中，使用了两个不同的EEG信号数据集来获得分类和可解释结果。推荐的XFE模型利用基于通道的最小和最大模式（ChMinMaxPat）特征提取函数，该函数从EEG数据中生成15个不同的特征向量。基于累积权重的邻域成分分析（CWNCA）用于从这些向量中选择最具信息性的特征。通过将基于迭代和集成t算法的k近邻（tkNN）分类器应用于每个特征向量来进行分类。信息融合通过迭代多数投票（IMV）实现，该方法整合了15个tkNN分类结果。最后，有向洛比什（DLob）符号语言通过利用所选特征的标识生成可解释的输出。tkNN分类器、基于IMV的信息融合和基于DLob的可解释特征提取共同将该模型转变为一个自组织可解释特征工程（SOXFE）框架。
基于ChMinMaxPat的模型在留一记录法（LORO）交叉验证（CV）下在两个数据集上的准确率均超过70%，在10折CV下的准确率超过90%。对于每个数据集，生成了15个DLob字符串，基于这些符号表示提供了可解释的输出。
基于ChMinMaxPat的SOXFE模型在从EEG信号中检测暴力和压力方面表现出高分类准确率和可解释性。该模型通过实现可解释的EEG分类，为特征工程和神经科学都做出了贡献，强调了EEG分析在临床和法医学应用中的潜在重要性。
