EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Padfield, Natasha; Zabalza, Jaime; Zhao, Huimin; Masero, Valentín; Ren, Jinchang

doi:10.3390/s19061423

Cited by 422 publications

(303 citation statements)

References 170 publications

Supporting

Mentioning

295

Contrasting

Unclassified

Order By: Relevance

“…The most frequently used portable BCI devices are based on electroencephalography (EEG). Although EEG provides excellent temporal resolution, making it ideal for real-time applications, the technology suffers from poor spatial resolution and an inherent sensitivity to motion artifacts (Padfield et al, 2019). Motion artifacts can have an impact on the spectral content of EEG in the frequency range below 20 Hz and lead to large spikes in the signal that may be difficult to correct (Mihajlovic et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

View full text Add to dashboard Cite

Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional nearinfrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean timeof-flight of photons was used to calculate the change in concentrations of oxy-and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

View full text Add to dashboard Cite

show abstract

“…These studies focused primarily on a few classes classification and non-intuitive tasks contained in BCI Competition IV data (left-hand, right-hand, foot, and tongue). However, intuitive MI is a practical BCI paradigm due to direct interaction between users and devices without artificial command matching [19]. To the best of our knowledge, these approaches have not achieved satisfactory classification performance on intuitive MI yet.…”

Section: Introductionmentioning

confidence: 99%

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Lee

Jeong

Shim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-toend role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.Index Terms-Brain-computer interface (BCI), Electroencephalogram (EEG), Motor imagery, Convolutional Neural Network (CNN)

show abstract

“…We followed the process discussed in Section V-A to estimate the posterior intensity of a persistence diagram D in H given a training set Q Y , with the goal of identifying the correct class of D. We used the R package BayesTDA to obtain posterior intensities. Consequently, the probability density was obtained from (2). After computing the intensities with respect to the training sets from both of the classes, the Bayes factor was computed by (3) as the ratio of the posterior probability densities of the unknown persistence diagram D given each of the two competing training sets from Q Y or Q Y .…”

Section: Eeg Signal Classification With Bayesian Learningmentioning

confidence: 99%

Bayesian Topological Learning for Brain State Classification

Nasrin¹,

Oballe²,

Boothe

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

Investigation of human brain states through electroencephalograph (EEG) signals is a crucial step in humanmachine communications. However, classifying and analyzing EEG signals are challenging due to their noisy, nonlinear and nonstationary nature. Current methodologies for analyzing these signals often fall short because they have several regularity assumptions baked in. This work provides an effective, flexible and noise-resilient scheme to analyze EEG by extracting pertinent information while abiding by the 3N (noisy, nonlinear and nonstationary) nature of data. We implement a topological tool, namely persistent homology, that tracks the evolution of topological features over time intervals and incorporates individual's expectations as prior knowledge by means of a Bayesian framework to compute posterior distributions. Relying on these posterior distributions, we apply Bayes factor classification to noisy EEG measurements. The performance of this Bayesian classification scheme is then compared with other existing methods for EEG signals.

show abstract

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Cited by 422 publications

References 170 publications

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Bayesian Topological Learning for Brain State Classification

Contact Info

Product

Resources

About