A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

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Abstract

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

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Deep learning with convolutional neural networks for EEG decoding and visualization

Robin Schirrmeister, Jost Tobias Springenberg, Lukas Fiederer … (2017)

Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.

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Author and article information

Contributors

Yvonne Tran: Role: Academic Editor

Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Journal ID (publisher-id): sensors

Title: Sensors (Basel, Switzerland)

Publisher: MDPI

ISSN (Electronic): 1424-8220

Publication date (Electronic): 20 March 2021

Publication date Collection: March 2021

Volume: 21

Issue: 6

Electronic Location Identifier: 2173

Affiliations

School of Fundamental Sciences, Massey University, 4410 Palmerston North, New Zealand; A.abdulhussain@ 123456massey.ac.nz (A.A.H.); S.Lal@ 123456massey.ac.nz (S.L.); H.W.Guesgen@ 123456massey.ac.nz (H.W.G.)

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[* ]Correspondence: A.Singh1@ 123456massey.ac.nz

Author information

Amardeep Singh https://orcid.org/0000-0003-1916-3347

Ali Abdul Hussain https://orcid.org/0000-0002-9814-9107

Sunil Lal https://orcid.org/0000-0002-1311-6710

Hans W. Guesgen https://orcid.org/0000-0002-8160-5946

Article

Publisher ID: sensors-21-02173

DOI: 10.3390/s21062173

PMC ID: 8003721

PubMed ID: 33804611

SO-VID: 5b479a3b-6376-43b6-a190-1137f4be15db

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

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Most cited references 204

A global geometric framework for nonlinear dimensionality reduction.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Deep learning with convolutional neural networks for EEG decoding and visualization

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