Deep Learning Classification Methods for Brain-Computer Interface: An Overview

2021 ◽  
pp. 117-124
Author(s):  
Sara Mohammed Farag ◽  
Samah Refat ◽  
Mohammed El-Telbany
Keyword(s):  
Deep Learning ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Methods

scholarly journals Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity

2019 ◽  
Vol 6 (2) ◽  
pp. 2084-2092 ◽  
Author(s):  
Xiang Zhang ◽  
Lina Yao ◽  
Shuai Zhang ◽  
Salil Kanhere ◽  
Michael Sheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Learning Framework

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

2018 ◽  
Vol 15 (3) ◽  
pp. 036028 ◽  
Author(s):  
Antonio Maria Chiarelli ◽  
Pierpaolo Croce ◽  
Arcangelo Merla ◽  
Filippo Zappasodi
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface

A Brain–Computer Interface Framework Based on Compressive Sensing and Deep Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 90-96
Author(s):  
Ritu Ranjan Shrivastwa ◽  
Vikramkumar Pudi ◽  
Chen Duo ◽  
Rosa So ◽  
Anupam Chattopadhyay ◽  
...  
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1736 ◽  
Author(s):  
Ikhtiyor Majidov ◽  
Taegkeun Whangbo
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Motor Imagery ◽  
Riemannian Geometry ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Swarm Optimization

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.

scholarly journals Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Nikolay V. Manyakov ◽  
Nikolay Chumerin ◽  
Adrien Combaz ◽  
Marc M. Van Hulle
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Methods ◽  
Linear Classifier ◽  
Typing System ◽  
The Individual ◽  
Lateral Sclerosis ◽  
Selection Of

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

scholarly journals Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

2019 ◽  
Vol 3 (3) ◽  
pp. 80
Author(s):  
Muhammad Fawaz Saputra ◽  
Noor Akhmad Setiawan ◽  
Igi Ardiyanto
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Short Term Memory ◽  
Brain Computer Interface ◽  
Correction Method ◽  
Computer Interface ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Learning Methods ◽  
Motor Movements

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM.

Development of a Deep Learning-Based Brain-Computer Interface for Visual Imagery Recognition

2020 ◽  
Author(s):  
Aldwin Jomar F. Castro ◽  
Justine Nicole P. Cruzit ◽  
Jerome Jeric C. De Guzman ◽  
John Jeru T. Pajarillo ◽  
Alyssa Margaux M. Rilloraza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Imagery ◽  
Brain Computer Interface ◽  
Computer Interface
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