عنوان مقاله فارسی: مروری بر معماری یادگیری عمیق برای تصویربرداری مغز مبتنی بر EEG

عنوان مقاله لاتین: Overview of Deep Learning Architectures for EEG-based Brain Imaging

نویسندگان: Lachezar Bozhkov; Petia Georgieva

تعداد صفحات: 7

سال انتشار: 2019

زبان: لاتین

Abstract:

Despite numerous successful applications of Deep Learning (DL) to large-scale image, video, speech and text data, they remain relatively unexplored in brain imaging field. In this paper, we make an overview of recent DL architectures for recognizing cognitive brain activities from Electroencephalogram (EEG) data with particular emphasis on Brain Computer Interface(BCI) technologies and Affective Neurocomputing. We discuss the use of convolutional, recurrent neural nets, as well as deep belief networks, echo-state networks, reservoir computing, and denoising auto encoder models. A major challenge in modeling brain cognitive activity from EEG data is finding representations that are invariant to inter- and intra-subject differences, as well as the inherent noise in the EEG recordings. The reviewed studies reveal the great potential of DL to decode human intentions in BCI applications and to find the invariant descriptors of human emotions across subjects in Affective Neurocomputing applications. Many of the DL models prove to be more accurate and efficient than traditional machine learning models.