scholarly journals Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6274
Author(s):  
Nayab Usama ◽  
Imran Khan Niazi ◽  
Kim Dremstrup ◽  
Mads Jochumsen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stroke Rehabilitation ◽  
Retest Reliability ◽  
Linear Discriminant ◽  
Temporal Features ◽  
Continuous Eeg ◽  
Related Potentials ◽  
Artificial Neural ◽  
Test Retest Reliability

Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

scholarly journals HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

2016 ◽  
Vol 38 (1) ◽  
pp. 151-157 ◽  
Author(s):  
BIANCA MACHADO CAMPOS ◽  
ALEXANDRE PIO VIANA ◽  
SILVANA SILVA RED QUINTAL ◽  
CIBELLE DEGEL BARBOSA ◽  
ROGÉRIO FIGUEIREDO DAHER
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Group Formation ◽  
Psidium Guajava ◽  
Optimal Number ◽  
Heterotic Group ◽  
Linear Discriminant ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Identification and Quantification of Industrial Grade Glycerol Adulteration in Red Wine with Fourier Transform Infrared Spectroscopy Using Chemometrics and Artificial Neural Networks

2005 ◽  
Vol 59 (12) ◽  
pp. 1553-1561 ◽  
Author(s):  
Vivechana Dixit ◽  
Jagdish C. Tewari ◽  
Byoung-Kwan Cho ◽  
Joseph M. K. Irudayaraj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Red Wine ◽  
Radial Basis Function Network ◽  
Fourier Transform Infrared ◽  
Linear Discriminant ◽  
Industrial Grade ◽  
Artificial Neural ◽  
Propagation Network

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900–1500 cm−1. Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis ( r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

Slum Dwellers Perception Towards Ujjwala Yojana, a Government-Lead LPG Intervention Programme in India: An Empirical Investigation

2021 ◽  
pp. 239448112110203
Author(s):  
Koustubh Kanti Ray
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Support Vector ◽  
Intervention Programme ◽  
Linear Discriminant ◽  
Customer Perception ◽  
Slum Dwellers ◽  
Functional Factors ◽  
Artificial Neural

Numerous studies are available in the academic literature that investigates the customer perception under different contexts. In the present research the researcher tries to investigate the customer perception towards the Indian Government-sponsored social programme from the slum dwellers’ prospective. The author believes that the customer perception towards the government-lead liquefied petroleum gas intervention programme is influenced by multiple functional factors. The functional factors include both process or delivery variables and the outcome factors. In order to test the hypothesis, machine learning binary classifiers like logit, support vector machine, linear discriminant analysis, quadratic discriminant analysis and artificial neural network models are adopted. The binary classifier model efficiencies are analysed with multiple performance measurement parameters like accuracy rate, error rate, F-score, precision, kappa coefficient, Matthews correlation coefficient and area under receiver operating characteristic. While evaluating between the degree of accuracy between actual and predicted cases, the model efficiency results indicate a better predictive power of the classifier models. In relative performance of classifier models, artificial neural network outperformed the other models adopted in the empirical research.

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