EEG Signals Classification related to Visual Objects using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression
Hajar Ahmadieh,
Farnaz Gassemi,
Mohammad Hasan Moradi
Abstract:By comprehending how brain activity is encoded and decoded, we can better comprehend how the brain functions. This study presents a method for classifying EEG signals from visual objects that combines an LSTM network with nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is used to extract features from the images, the LSTM network is used to extract features from the EEG signal, and NIT2FR is used to map the features from the images to the features from the EEG signal. In this paper, type-2 fu… Show more
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