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- W4226541586 abstract "An electroencephalogram (EEG) is used to evaluate the electrical activity of the brain. While a person sees something, the brain creates a mental precept, this precept captures the use of EEG to get an instantaneous example of what is happening inside the brain all through the precise technique. This study aims to design an automated convolutional neural network (CNN)-based deep learning algorithm that can be employed for the visual classification of a human facial portrait using electroencephalography processing. Moreover, EEG information evoked through visible photograph stimuli has been employed through the convolution neural network (CNN) to realize a discriminatory mind recreation manifold of image classifications within the mind-reading process. We have used a 9-channel EEG Mindwave Mobile 2 headset to record the brain activity of subjects while looking at images of four persons from the dataset. The presented results validate the proposed algorithm as it shows a precision of 80% that has greatly outshined the existing techniques. In further, this study shows that the learned capabilities by CNN-based deep learning models can be employed for automated visual classification that can be used for disabled persons and criminal investigation with further few improvements." @default.
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- W4226541586 date "2022-01-01" @default.
- W4226541586 modified "2023-10-14" @default.
- W4226541586 title "Classification of Human Facial Portrait Using EEG Signal Processing and Deep Learning Algorithms" @default.
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- W4226541586 doi "https://doi.org/10.1007/978-981-16-9873-6_55" @default.
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