Mohammad Almasi - Department of Mathematical Engineering, University Polytechnic of Catalonia, Spain
Roghayeh rezaei - Department of Computer Engineering, Technicaland Vocational University of kosar, Iran
Nastaran Saleh - Department of Biomedical Engineering,Islamic Azad University Branch of Tehran South, Iran

This article aims to use the processing of electroencephalogram (EEG) signals of images generated by the brain. This study expands an existing solution by exploring the gains of adapting classifier parameters to the user. In the first step, we developed a deep learning model that extracts features from raw EEG signals and predicts the image among ۴۰ possible classes from the ImageNet dataset. The main goal of this paper is to adapt this model to new users to create a unique model based on the minimum number of new users.

Deep Learning, LSTM, EEG signal, ImageNet, Machine Learning, Electroencephalography.