همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Reconstruction of ECoG signals in response to visual stimuli using a model based on convolutional and regression networks.

نویسندگان :

Mohammad Amin Lotfi¹ Kimiya ٍEghbal² Fateneh Zareayan Jahromy³

1- Biomedical Engineering Department, School of Electrical Engineering Iran University of Science and Technology (IUST) Tehran, Iran 2- Biomedical Engineering Department, School of Electrical Engineering Iran University of Science and Technology (IUST) Tehran, Iran 3- Biomedical Engineering Department, School of Electrical Engineering Iran University of Science and Technology (IUST) Tehran, Iran

کلمات کلیدی :

Convolutional Neural Networks،electrocorticography،Regressor،vision

چکیده :

The visual system is one of the most sophisticated complex systems in our body, and it plays a crucial role in enabling us to perceive the world around us. When we see images, we send visual information from the eyes to different parts of the brain and various routes transmit visual information and processing. The purpose of this study is to ascertain whether it is possible to reconstruct brain signals directly from visual stimuli using deep neural networks. In order to simulate the visual routes in the brain, we implemented deep neural networks (DNNs) with the objective of predicting the electrocortical data of the whole brain of the Subjects. In this study, we employed an advanced methodology that utilized convolutional neural networks to decode the electrical activity of the brain during the processing of visual data. A convolutional neural network is employed to extract relevant features from the image, which are then fed to a deep regressor for the prediction of the electrocortical data of the subject in that trial. The results demonstrated that brain signals could be reconstructed directly from visual stimuli presented in the trial with acceptable efficiency. Furthermore, neural routes in the brain could be simulated via DNNs. This model could facilitate a deeper understanding of human vision and enhance our comprehension of data processing within the brain.