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Submitted
July 20, 2025
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January 26, 2026
Published
June 1, 2026
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Abstract

Further evidence suggests that smart appliances with several types of controllability are necessary, such as smart homes. This practical approach will be greatly appreciated by those who cannot access control of devices that require direct human engagement. This study proposes and constructs a smart home setup based on enhanced electroencephalography (EEG), which could potentially assist individuals with or without movement problems to manage gadgets with greater comfort and ease. In this study, motor/imaginary EEG (MI-EEG) datasets were used. This dataset includes 25 subjects who completed motor and imagery tasks. EEG signals were captured using a 64-channel (BCI2000) instrument. The features are extracted from the CNN's convolutional layers, which are then used to train the classifier. Features are extracted from both the test and training datasets, and the labels are flattened to match the expected input format for the classifier. In addition, the proposed Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Linear Discriminant Analysis (LDA) models can remove the excessive noise and overlaps that lead to misclassification and classify the chosen features into separate classes. Based on the number of classes reached by Arduino UNO, the identified classes are tasked with controlling the smart home. The high overall accuracies using SVM, CNN, and LDA are (99%, 96%, 99%), respectively. According to the current research, the suggested approach for classifying MI-EEG is successful. It may be suitable for BCI applications, allowing for controlling a smart home via brain waves. Results from this study support the idea that the proposed EEG-based smart home could be helpful for older people and others with mobility issues in the future.

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