A review on EEG based brain computer interface systems feature extraction methods

2016 ◽  
Vol 04 (02) ◽  
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
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 Brain-Computer Interface: Advancement and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5746
Author(s):  
M. F. Mridha ◽  
Sujoy Chandra Das ◽  
Muhammad Mohsin Kabir ◽  
Aklima Akter Lima ◽  
Md. Rashedul Islam ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Computer Interface ◽  
Comprehensive Overview ◽  
Comprehensive Review ◽  
Biomedical Sensors ◽  
Active Research ◽  
Evaluation Measurement ◽  
Research Domain

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

Introduction to Motor Imagery-Based Brain-Computer Interface

2020 ◽  
pp. 168-197
Author(s):  
Nitesh Singh Malan ◽  
Shiru Sharma
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Complex Wavelet Transform ◽  
Feature Extraction Method ◽  
Time Frequency

In this chapter, motor imagery (MI) based brain-computer interface (BCI) is introduced incorporating the explanation of key components required to design a practical BCI device. Its application to the medical and nonmedical sector is discussed in detail. In the experimental study, a feature extraction method using time, frequency, and phase analysis of Motor imagery EEG is presented. For the classification of MI task, EEG signals are decomposed using a dual-tree complex wavelet transform (DTCWT) and then time, frequency, and phase features are extracted. The validation of the proposed method is conducted using BCI competition IV dataset 2b. A Support vector machine (SVM) classifier is used to perform the classification task. Performance of the proposed method is compared with the standard feature extraction methods. The proposed scheme achieved a larger average classification accuracy of 82.81% which is better than that obtained by other methods.

scholarly journals Processing EEG signals acquired from a consumer grade BCI device

2018 ◽  
Vol 11 (2) ◽  
pp. 29-34 ◽  
Author(s):  
Fanny Monori ◽  
Stefan Oniga
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Human Brain ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Multiclass Classification ◽  
Computer Interface ◽  
Clinical Settings ◽  
Eeg Signals ◽  
Complex Information

Abstract BCI (Brain-Computer Interface) is a technology which goal is to create and manage a connection between the human brain and a computer with the help of EEG signals. In the last decade consumer-grade BCI devices became available thus giving opportunity to develop BCI applications outside of clinical settings. In this paper we use a device called NeuroSky MindWave Mobile. We investigate what type of information can be deducted from the data acquired from this device, and we evaluate whether it can help us in BCI applications. Our methods of processing the data involves feature extraction methods, and neural networks. Specifically, we make experiments with finding patterns in the data by binary and multiclass classification. With these methods we could detect sharp changes in the signal such as blinking patterns, but we could not extract more complex information successfully.

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