Feature Extraction and Classification of Mental EEG for Motor Imagery

2009 ◽  
Author(s):  
Li Ming-Ai ◽  
Wang Rui ◽  
Hao Dong-Mei ◽  
Yang Jin-Fu
Keyword(s):  
Feature Extraction ◽  
Motor Imagery

Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data

2019 ◽  
Vol 64 (6) ◽  
pp. 643-653
Author(s):  
Funda Kutlu Onay ◽  
Cemal Köse
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Channel Selection ◽  
Support Vector ◽  
Local Transformation ◽  
Two Stage ◽  
Linear Discriminant ◽  
One Dimensional ◽  
Brain Functions

Abstract The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.

Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

2015 ◽  
Vol 25 (14) ◽  
pp. 1540023
Author(s):  
Germán Rodríguez-Bermúdez ◽  
Miguel Ángel Sánchez-Granero ◽  
Pedro J. García-Laencina ◽  
Manuel Fernández-Martínez ◽  
José Serna ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Information Acquisition ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Self Similarity ◽  
Self Similar ◽  
Bci System

A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

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