scholarly journals Novel Features for Brain-Computer Interfaces

2007 ◽  
Vol 2007 ◽  
pp. 1-7 ◽  
Author(s):  
W. L. Woon ◽  
A. Cichocki
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Test Results ◽  
Brain Computer Interfaces ◽  
Extraction Techniques ◽  
Computer Interfaces ◽  
Nonlinear Features ◽  
Current Feature

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques.

Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces

2019 ◽  
Vol 57 (8) ◽  
pp. 1709-1725 ◽  
Author(s):  
Paula G. Rodrigues ◽  
Carlos A. Stefano Filho ◽  
Romis Attux ◽  
Gabriela Castellano ◽  
Diogo C. Soriano
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Space Time ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces

2014 ◽  
Vol 556-562 ◽  
pp. 2829-2833 ◽  
Author(s):  
Bang Hua Yang ◽  
Ting Wu ◽  
Qian Wang ◽  
Zhi Jun Han
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Kernel Function ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Brain Computer Interfaces ◽  
Recognition Method ◽  
Computer Interfaces

A recognition method based on Wavelet Packet Decomposition - Common Spatial Patterns (WPD-CSP) and Kernel Fisher Support Vector Machine (KF-SVM) is developed and used for EEG recognition in motor imagery brain–computer interfaces (BCIs). The WPD-CSP is used for feature extraction and KF-SVM is used for classification. The presented recognition method includes the following steps: (1) some important EEG channels are selected. The 'haar' wavelet basis is used to take wavelet packet decomposition. And some decomposed sub-bands related with motor imagery for each EEG channel are reconstructed to obtain the relevant frequency information. (2) A six-dimensional feature vector is obtained by the CSP feature extraction to the reconstructed signal. And then the within-class scatter is calculated based on the feature vector. (3) The scatter is added into the radical basis function to construct a new kernel function. The obtained new kernel is integrated into the SVM to act as its kernel function. To evaluate effectiveness of the proposed WPD-CSP + KF-SVM method, the data from the 2008 international BCI competition are processed. A preliminary result shows that the proposed classification algorithm can well recognize EEG data and improve the EEG recognition accuracy in motor imagery BCIs.

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