DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

作     者：Chandralekha, M. Jayadurga, N. Priyadharshini Chen, Thomas M. Sathiyanarayanan, Mithileysh 

作者机构：Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India School of Science & Technology University of London London United Kingdom Research & Innovation MIT Square London United Kingdom 

出 版 物：《International Journal of Information Technology (Singapore)》 (Int. J. Inf. Technol.)

年 卷 期：2025年第17卷第4期

页      面：2015-2033页

主　　题：Brain-Computer Interface (BCI) Cuckoo Search Algorithm (CSA) DCTCS Stack Classifier Discrete Cosine Transformation (DCT) Electroencephalogram (EEG) Eye state Classification Stacked Machine Learning Model 

摘      要：Accurate classification of eye state using Electroencephalogram (EEG) data reflects the high dimensionality and intricacy associated with the nature of the EEG signals. This state-of-the-art technique applies Discrete Cosine Transformation (DCT) for feature extraction, Cuckoo Search Algorithm (CSA) for optimal feature selection followed by a robust stacking algorithm integrating Adaptive Gradient Boosting Classifier (A-GBC) and k-Nearest Neighbors (k-NN) with Logistic Regression as meta-learner to improve the predictive accuracy and model performance. This model would strengthen the accuracy, reliability, and efficiency of eye state classification. DCT is used to compress the time series data into frequency domain for feature extraction, resulting in concentrating a vast amount of superficial signal energy. The next stage uses the Cuckoo Search Algorithm (CSA) in the selection of the optimal features. It thus optimizes the feature sub- set of the input in-order to get an optimal learning rate. The combination of these processes with a stacked ensemble using Adaptive Gradient Boosting Classifier (A-GBC) & k-Nearest Neighbors (k-NN) basal learners results in an immediate classification following the path of a Logistic Regression meta learner that achieves a remarkable classification accuracy of 96.96%. This strongly emphasizes the exceptional efficiency of the proposed framework when placed in context with existing approaches. It implies that the research not only reinforces the existing state of EEG eye state categorization but also provides for the optimization of its utility in other types of biomedical signal processing, thereby making it an example of a versatile medical tool. © Bharati Vidyapeeth s Institute of Computer Applications and Management 2024.