Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

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A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

Brain Sciences ◽

10.3390/brainsci10100734 ◽

2020 ◽

Vol 10 (10) ◽

pp. 734

Author(s):

Md Rakibul Mowla ◽

Jesus D. Gonzalez-Morales ◽

Jacob Rico-Martinez ◽

Daniel A. Ulichnie ◽

David E. Thompson

Keyword(s):

Discriminant Analysis ◽

System Performance ◽

Brain Computer Interface ◽

Computer Interface ◽

Linear Discriminant ◽

Computer Interfaces ◽

Classification Technique ◽

Latency Jitter ◽

Bci System

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

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Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

Discrete Dynamics in Nature and Society ◽

10.1155/2011/724697 ◽

2011 ◽

Vol 2011 ◽

pp. 1-8 ◽

Cited By ~ 22

Author(s):

Chu Kiong Loo ◽

Andrews Samraj ◽

Gin Chong Lee

Keyword(s):

Feature Extraction ◽

Fractal Dimension ◽

Motor Imagery ◽

Classification Accuracy ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Estimation Methods ◽

Support Vector ◽

Linear Discriminant

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

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On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Sensors ◽

10.3390/s20164452 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4452 ◽

Cited By ~ 1

Author(s):

José-Vicente Riquelme-Ros ◽

Germán Rodríguez-Bermúdez ◽

Ignacio Rodríguez-Rodríguez ◽

José-Víctor Rodríguez ◽

José-María Molina-García-Pardo

Keyword(s):

Motor Imagery ◽

Brain Plasticity ◽

Brain Computer Interface ◽

Musical Training ◽

Machine Learning Algorithms ◽

Computer Interface ◽

Control Group ◽

Eeg Signals ◽

Linear Discriminant ◽

Electrical Brain Activity

Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.

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Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis

Journal of Medical Systems ◽

10.1007/s10916-019-1270-0 ◽

2019 ◽

Vol 43 (6) ◽

Cited By ~ 9

Author(s):

Rongrong Fu ◽

Yongsheng Tian ◽

Tiantian Bao ◽

Zong Meng ◽

Peiming Shi

Keyword(s):

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Motor Imagery ◽

Linear Discriminant ◽

Eeg Classification ◽

Regularized Linear Discriminant Analysis

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EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

Advances in Abstract Intelligence and Soft Computing ◽

10.4018/978-1-4666-2651-5.ch005 ◽

2013 ◽

pp. 57-69

Author(s):

Ling Zou ◽

Xinguang Wang ◽

Guodong Shi ◽

Zhenghua Ma

Keyword(s):

Support Vector Machine ◽

Motor Imagery ◽

Brain Computer Interface ◽

Average Power ◽

Computer Interface ◽

Support Vector ◽

Discrete Wavelet ◽

Classification Rate ◽

Transform Method ◽

Linear Discriminant

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

Download Full-text