Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Maham Saeidi,Redha Taiar,Awad Al-Juaid,P A Hancock,Krzysztof Fiok,Waldemar Karwowski,Farzad V Farahani

doi:10.3390/brainsci11111525

Maham Saeidi, Redha Taiar + Show 5 more

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https://doi.org/10.3390/brainsci11111525

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Abstract

Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

Highlights

The human brain is a complex system containing approximately 100 billion neurons and trillions of synaptic connections [1,2]
To determine which articles were appropriate to consider in this study, we reviewed all abstracts to determine whether the findings met our inclusion criteria
We introduced the publicly available databases that have frequently been used for each task, and we directly analyzed the classification performance reported in relevant studies

Summary

Introduction

The human brain is a complex system containing approximately 100 billion neurons and trillions of synaptic connections [1,2]. The brain’s electrical activity became a research focus in the 19th century when Richard Caton recorded brain signals from rabbits [3,4]. Brain recordings were performed by Hans Berger, the first person to record electroencephalogram (EEG) signals from the human scalp [5]. EEG-based research has since increased significantly, and EEG is the most commonly used noninvasive tool to study dynamic signatures in the human brain [6,7]. Signals traveling in white matter have traditionally been thought to be too fast to superimpose temporally, recent cable theoretic models [9] and empirical work [10] suggest that white matter may contribute to brain rhythms measured at the scalp. The three primary forms of the brain’s activity based on EEG signals are brain waves, event-related potential (ERP), and steady-state visual evoked potentials (SSVEPs). Brain waves are most commonly used in EEG signal analysis for different types of tasks. Other classifications of brain signals can be found in previous publications [12,13]

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