Motor Imagery (MI) based processes are most commonly used in Brain-Computer Interfaces (BCIs) and these systems are commonly applied in the military, medicine, rehabilitation, and so on. A large number of works have been provided in the past using Electroencephalograms (EEG), however, the presence of artifacts and correlation among the signals limits their performance. This work aims to design an effective brain activity detection model using a hybrid Deep Learning (DL)-based model called Quantum Fusion Maxout Network (QFMNet) for accurately detecting the brain activity of individuals using MI based on EEG signals. Here, initially, the input EEG signal obtained from the dataset is given to the preprocessing stage, where noise is removed from the input signal using a Gaussian filter. Then, the preprocessed signal is subjected to a feature extraction process, and subsequently, data augmentation is done. Finally, brain activity detection is performed using the proposed QFMNet, where the proposed QFMNet is developed using Deep Quantum Neural Network (DQNN) and Deep Maxout Network (DMN). The QFMNet is observed to have higher values of specificity of 93.8%, accuracy of 93.0%, and sensitivity of 94.8%, by considering the k-group as 9.
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