article · Scientific Reports
A novel metaheuristic method called the Modified Al-Biruni Earth Radius (MBER) algorithm has been developed to optimise the classification of eye states as open or closed using electroencephalogram (EEG) data. Tested on an available preprocessed EEG dataset, the binary version of the algorithm identifies key features to boost classification precision. The approach was benchmarked against five established optimisation methods, including Particle Swarm Optimisation, Grey Wolf Optimiser, and Genetic Algorithms, with performance confirmed through statistical tests. When paired with various machine learning classifiers, a K-Nearest Neighbours model delivered the strongest performance and was adopted as the fitness function. Through this combination, the optimiser achieved an eye state classification accuracy of 96.12 percent alongside high sensitivity and specificity.
Determining whether eyes are open or closed from brainwave activity is a fundamental task in brain-computer interfaces and gaze-tracking systems. By effectively selecting relevant EEG features, this algorithm increases classification accuracy, helping to make automated interpretation of complex neurological signals more dependable.
The method could support software developers creating assistive communication tools or hands-free control systems that interpret brain activity. Because the results are based solely on an offline, preprocessed dataset, the research remains at an early algorithmic stage, requiring real-time testing and integration with physical EEG hardware before practical deployment.
AI-generated from the published abstract. Always read the original work before citing.
This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm's binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1038/s41598-024-74475-5
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
