scholarly journals Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces

2021 ◽  
Vol 13 (8) ◽  
pp. 202
Author(s):  
David Balderas ◽  
Pedro Ponce ◽  
Diego Lopez-Bernal ◽  
Arturo Molina
Keyword(s):  
Eye Movement ◽  
Engineering Students ◽  
Real Life ◽  
Support Vector ◽  
Brain Computer Interfaces ◽  
Restricted Boltzmann Machines ◽  
Significant Information ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Scientists And Engineers

Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0.

A Review of Current Approaches of Brain Computer Interfaces

2013 ◽  
pp. 1516-1534
Author(s):  
Lochi Yu ◽  
Cristian Ureña
Keyword(s):  
Brain Computer Interface ◽  
Severe Disabilities ◽  
Extraction Methods ◽  
Support Vector ◽  
Brain Electrical Activity ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Computational Resources ◽  
The Brain

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

scholarly journals Improvement of P300-Based Brain–Computer Interfaces for Home Appliances Control by Data Balancing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5576
Author(s):  
Taejun Lee ◽  
Minju Kim ◽  
Sung-Phil Kim
Keyword(s):  
Information Transfer ◽  
Regularization Parameter ◽  
Support Vector ◽  
Oddball Paradigm ◽  
Svm Classifier ◽  
Sampling Techniques ◽  
Brain Computer Interfaces ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Computer Interfaces

The oddball paradigm used in P300-based brain–computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek’s links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem.

A Review of Current Approaches of Brain Computer Interfaces

2012 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Lochi Yu ◽  
Cristian Ureña
Keyword(s):  
Severe Disabilities ◽  
Extraction Methods ◽  
Support Vector ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Different Types ◽  
Computational Resources ◽  
The Brain

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

scholarly journals Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74433 ◽  
Author(s):  
Rui Zhang ◽  
Peng Xu ◽  
Lanjin Guo ◽  
Yangsong Zhang ◽  
Peiyang Li ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Brain Computer Interfaces ◽  
Z Score ◽  
Linear Discriminant ◽  
Computer Interfaces

Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms

2019 ◽  
Vol 51 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Jobin T. Philip ◽  
S. Thomas George
Keyword(s):  
Information Transfer ◽  
Performance Metrics ◽  
Support Vector ◽  
Classification Algorithms ◽  
Special Focus ◽  
Brain Computer Interfaces ◽  
Human Beings ◽  
Computer Interfaces ◽  
Recent Developments ◽  
Visual P300

Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities of human beings. The visual P300 mind-speller is a prominent one among them, which has opened up tremendous possibilities in movement and communication applications. Today, there exist many state-of-the-art visual P300 mind-speller implementations in the literature as a result of numerous researches in this domain over the past 2 decades. Each of these systems can be evaluated in terms of performance metrics like classification accuracy, information transfer rate, and processing time. Various classification techniques associated with these systems, which include but are not limited to discriminant analysis, support vector machine, neural network, distance-based and ensemble of classifiers, have major roles in determining the overall system performances. The significance of a proper review on the recent developments in visual P300 mind-spellers with proper emphasis on their classification algorithms is the key insight for this work. This article is organized with a brief introduction to P300, concepts of visual P300 mind-spellers, the survey of literature with special focus on classification algorithms, followed by the discussion of various challenges and future directions.

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