scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021 ◽  
Vol 11 (23) ◽  
pp. 11252
Author(s):  
Ayana Mussabayeva ◽  
Prashant Kumar Jamwal ◽  
Muhammad Tahir Akhtar
Keyword(s):  
Healthy Subjects ◽  
Single Channel ◽  
Event Related Potentials ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Related Potentials ◽  
Als Patients

Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.

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 SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL

2017 ◽  
Vol 10 (13) ◽  
pp. 137
Author(s):  
Darshan A Khade ◽  
Ilakiyaselvan N
Keyword(s):  
Artificial Intelligence ◽  
Event Related Potentials ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signals ◽  
Future Technology ◽  
Brain Waves ◽  
Brain Signals ◽  
Related Potentials ◽  
Signal Machine

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. 

scholarly journals Detection of event-related potentials in individual subjects using support vector machines

2014 ◽  
Vol 2 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Hossein Parvar ◽  
Lauren Sculthorpe-Petley ◽  
Jason Satel ◽  
Rober Boshra ◽  
Ryan C. N. D’Arcy ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Event Related Potentials ◽  
Support Vector ◽  
Vector Machines ◽  
Related Potentials

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 Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020 ◽  
Author(s):  
Jan Sosulski ◽  
Jan-Philipp Kemmer ◽  
Michael Tangermann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Covariance Matrix ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Feature Representation ◽  
Training Data ◽  
Linear Discriminant ◽  
Related Potentials

AbstractElectroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

Feature Reduction for Computationally Efficient Damage State Classification Using Binary Tree Support Vector Machines

2008 ◽  
Author(s):  
Clyde Coelho ◽  
Aditi Chattopadhyay
Keyword(s):  
Support Vector Machines ◽  
Binary Tree ◽  
Feature Reduction ◽  
Training Data ◽  
Support Vector ◽  
Computationally Efficient ◽  
Damage State ◽  
Data Set ◽  
Linear Discriminant ◽  
Vector Machines

This paper proposes a computationally efficient methodology for classifying damage in structural hotspots. Data collected from a sensor instrumented lug joint subjected to fatigue loading was preprocessed using a linear discriminant analysis (LDA) to extract features that are relevant for classification and reduce the dimensionality of the data. The data is then reduced in the feature space by analyzing the structure of the mapped clusters and removing the data points that do not affect the construction of interclass separating hyperplanes. The reduced data set is used to train a support vector machines (SVM) based classifier and the results of the classification problem are compared to those when the entire data set is used for training. To further improve the efficiency of the classification scheme, the SVM classifiers are arranged in a binary tree format to reduce the number of comparisons that are necessary. The experimental results show that the data reduction does not reduce the ability of the classifier to distinguish between classes while providing a nearly fourfold decrease in the amount of training data processed.

Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

2020 ◽  
Vol 42 (11) ◽  
pp. 2057-2067
Author(s):  
Moon Inder Singh ◽  
Mandeep Singh
Keyword(s):  
Event Related Potentials ◽  
Central Line ◽  
Support Vector ◽  
Eeg Signals ◽  
High Arousal ◽  
Brain Signals ◽  
Polynomial Classifier ◽  
Related Potentials ◽  
Daunting Challenge

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

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