Feature Extraction and Pattern Classification on Mining Electroencephalography Data for Brain-Computer Interface

This paper presents the classification of three mental tasks, using the EEG signal and simulating a real-time process, what is known as pseudo-online technique. The Bayesian classifier is used to recognize the mental tasks, the feature extraction uses the Power Spectral Density, and the Sammon map is used to visualize the class separation. The choice of the EEG channel and sampling frequency is based on the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifications.

Download Full-text

Massively Parallel Signal Processing Using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction

Frontiers in Neuroengineering ◽

10.3389/neuro.16.011.2009 ◽

2009 ◽

Vol 2 ◽

Cited By ~ 26

Author(s):

J. Adam Wilson

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

Real Time ◽

Graphics Processing Unit ◽

Brain Computer Interface ◽

Massively Parallel ◽

Computer Interface ◽

Processing Unit ◽

Graphics Processing ◽

Parallel Signal Processing

Download Full-text

A Recurrence-Based Approach for Feature Extraction in Brain-Computer Interface Systems

Springer Proceedings in Mathematics & Statistics - Translational Recurrences ◽

10.1007/978-3-319-09531-8_6 ◽

2014 ◽

pp. 95-107 ◽

Cited By ~ 3

Author(s):

Luisa F. S. Uribe ◽

Filipe I. Fazanaro ◽

Gabriela Castellano ◽

Ricardo Suyama ◽

Romis Attux ◽

...

Keyword(s):

Feature Extraction ◽

Brain Computer Interface ◽

Computer Interface

Download Full-text

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Sensors ◽

10.3390/s19071736 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1736 ◽

Cited By ~ 11

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.

Download Full-text