Deep Learning Algorithms in EEG Signal Decoding Application: A Review

Vyom Kulshreshtha,Koya Jeevan Reddy,Ramesh Babu Vallabhaneni,Pankaj Sharma,Surendra Kumar Bitra,S Selva Kumar,Vinit Kumar,V Sandeep Kumar

doi:10.1109/access.2021.3105917

Abstract

In recent years, deep learning algorithms have been developed rapidly, and they are becoming a powerful tool in biomedical engineering. Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological or pathological status of the brain from electroencephalographic (EEG). This paper overviews current application of deep learning algorithms in various EEG decoding tasks, and introduces commonly used algorithms, typical application scenarios, important progresses and existing problems. Firstly, the basic principles of deep learning algorithms used in EEG decoding is briefly described, including convolutional neural network, deep belief network, auto-encoder and recurrent neural network. In this paper, existing applications of deep learning on EEG is discussed, including brain-computer interfaces, cognitive neuroscience and diagnosis of brain disorders. Finally, this paper outlines some key problems that will be addressed in future applications of deep learning for EEG decoding, such as parameter selection, computational complexity, and the capability of generalization.

Highlights

Electroencephalogram (EEG) is a spontaneous and rhythmic electrical activity of the brain [1,2]
Compared with other brain imaging functions, such as intra cortical neural recording, functional near-infrared spectroscopy and magnetic resonance imaging, the EEG is used in the research and development of rehabilitation equipment, such as the development of brain-computer interface (BCI) and neuro feedback technologies to achieve the recovery of patients' motor cognition and other functions [7]
In the above clinical application and scientific research of EEG, the machine learning algorithms are often used to decode EEG signals to accurately identify physiological or pathological conditions

Summary

INTRODUCTION

Electroencephalogram (EEG) is a spontaneous and rhythmic electrical activity of the brain [1,2]. EEG is a brain imaging method that uses electrodes attached to surface of scalp to identify and record electrical activity signals of neuronal clusters in the cerebral cortex through precise electronic measurement technology, which can obtain brain idea and cognition. In the above clinical application and scientific research of EEG, the machine learning algorithms are often used to decode EEG signals to accurately identify physiological or pathological conditions. Shortcomings of less spatial resolution and signalto-noise ratio (SNR) of EEG signals [8], the accuracy of machine learning decoding has greater limitations, causing many difficulties in practical applications. The following first introduces the traditional algorithms in machine learning are applied to VOLUME XX, 2017. EEG decoding, and explains advantages of deep learning based on its limitations in practical applications, and briefly describes the basic principles of the deep learning algorithms currently applied in EEG decoding, and introduces these.

TRADITIONAL MACHINE LEARNING ALGORITHM APPLIED TO EEG DECODING

APPLICATION OF DEEP LEARNING ALGORITHM IN EEG DECODING

BRAIN-COMPUTER INTERFACE

CONCLUSION