scholarly journals Using a machine learning approach to complement group level statistics in experimental psychology: A case study to reveal different levels of inhibition in a modified Flanker Task

2018 ◽  
Author(s):  
Tanja Krumpe ◽  
Christian Scharinger ◽  
Wolfgang Rosenstiel ◽  
Peter Gerjets ◽  
Martin Spueler
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Inhibitory Control ◽  
Flanker Task ◽  
Mental States ◽  
Support Vector ◽  
Group Level ◽  
Single Trial ◽  
Level Statistics ◽  
Different Levels

In this paper, we demonstrate how machine learning (ML) can be used to beneficially complement the traditional analysis of behavioral and physiological data to provide new insights into the structure of mental states, in this case, executive functions (EFs) with a focus on inhibitory control. We used a modified Flanker task with the aim to distinguish three levels of inhibitory control: no inhibition, readiness for inhibition and the actual execution of inhibitory control. A simultaneously presented n-back task was used to additionally induce demands on a second executive function. This design enabled us to investigate how the overlap of resources influences the distinction between three levels of inhibitory control. A support vector machine (SVM) based classification approach has been used on EEG data to predict the level of inhibitory control on single-subject and single-trial level. The SVM classification is a subject-specific and single-trial based approach which will be compared to standard group-level statistical approaches to reveal that both methodologies access different properties of the data. We show that considering both methods can give new insights into mental states which cannot be discovered when only using group-level statistics alone. Machine learning results indicate that three different levels of inhibitory control can be distinguished, while the group-average analysis does not give rise to this assumption. In addition, we highlight one other important benefit of the ML approach. We are able to define specific properties of the executive function inhibition by investigating the neural activation patterns that were used during the classification process.

scholarly journals Single-Trial Decoding of Scalp EEG under Natural Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Nicolai Pedersen ◽  
Dea Steenstrup ◽  
Lars Kai Hansen
Keyword(s):  
Signal To Noise Ratio ◽  
Mental States ◽  
Small Samples ◽  
Support Vector ◽  
Large Set ◽  
Learning Approaches ◽  
Single Trial ◽  
Scalp Eeg ◽  
Dry Electrode ◽  
Sensitivity Maps

There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.

scholarly journals Study of Machine Learning Techniques for EEG Eye State Detection

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 53
Author(s):  
Francisco Laport ◽  
Paula M. Castro ◽  
Adriana Dapena ◽  
Francisco J. Vazquez-Araujo ◽  
Daniel Iglesia
Keyword(s):  
Machine Learning ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Linear Discriminant ◽  
State Identification ◽  
Brain Signals ◽  
State Classification ◽  
Learning Techniques

A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.

scholarly journals Does Language Make a Difference? A Study of Language Dominance and Inhibitory Control

2021 ◽  
Vol 12 ◽  
Author(s):  
Ashley Marie Salwei ◽  
Beatriz de Diego-Lázaro
Keyword(s):  
Executive Function ◽  
Inhibitory Control ◽  
Task Analysis ◽  
Maternal Education ◽  
Flanker Task ◽  
Bilingual Children ◽  
Expressive Vocabulary ◽  
Control Task ◽  
Dominant Language ◽  
Partial Correlations

Although extensive research has been done to compare monolingual and bilingual children’s executive function, there are fewer studies that look at the relation between bilingual children’s languages and executive function. The purpose of this study was two-fold; first, to compare inhibitory control (executive function) in monolingual and bilingual children and second, to determine what vocabulary measure (dominant vs. non-dominant language) was related to inhibitory control in bilingual children. Twenty monolingual (English) and 20 bilingual (English-Spanish) children between the ages of 8 and 12 completed a vocabulary test (in English and Spanish) and an inhibitory control task (the flanker task). Analysis of Covariances (ANCOVAs) revealed no significant differences between monolingual and bilingual children in reaction time (RT) or accuracy in the flanker task after controlling for maternal education. Partial correlations controlling for age showed that English expressive vocabulary (dominant language), but not Spanish, was positively correlated with inhibitory control (larger vocabulary and better inhibitory control), suggesting that bilingual children may use their dominant language to self-regulate over their non-dominant language, increasing the inhibitory control associated to the dominant language.

scholarly journals Single-Trial Decoding of Scalp EEG Under Natural Conditions

2018 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Nicolai Pedersen ◽  
Dea Steenstrup ◽  
Lars Kai Hansen
Keyword(s):  
Signal To Noise Ratio ◽  
Mental States ◽  
Small Samples ◽  
Support Vector ◽  
Large Set ◽  
Learning Approaches ◽  
Single Trial ◽  
Scalp Eeg ◽  
Dry Electrode ◽  
Sensitivity Maps

ABSTRACTThere is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. EEG signals are subject-specific, sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that Support Vector Machine (SVM) classifiers trained on a relatively small set of de-noised (averaged) pseudo-trials perform on par with classifiers trained on a large set of noisy single-trial samples. For visualization of EEG signatures exploited by SVM classifiers, we propose a novel method for computing sensitivity maps of EEG-based SVM classifiers. Moreover, we apply the NPAIRS resampling framework for estimation of map uncertainty and show that effect sizes of sensitivity maps for classifiers trained on small samples of de-noised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudo-trial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization and unbiased performance evaluation in machine learning approaches for brain decoding.

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

2019 ◽  
Vol 27 (7) ◽  
pp. 1492-1501 ◽  
Author(s):  
Rober Boshra ◽  
Kiret Dhindsa ◽  
Omar Boursalie ◽  
Kyle I. Ruiter ◽  
Ranil Sonnadara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Subject ◽  
Group Level ◽  
Level Statistics

scholarly journals Comparison of Machine Learning Analysis on Predictive Factors of Children’s Planning-Organizing Executive Function by Income Level: Through Home Environment Quality and Wealth Factors

2021 ◽  
Vol 24 (6) ◽  
pp. 651-662
Author(s):  
Hye-Kyung Lim ◽  
Hyun-Ok Kim ◽  
Hae-Seon Park
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Executive Functions ◽  
Home Environment ◽  
Income Group ◽  
Support Vector ◽  
High Income Group ◽  
Environment Quality ◽  
Emotional Environment ◽  
Learning Reinforcement

Background and objective: This study identifies whether children's planning-organizing executive function can be significantly classified and predicted by home environment quality and wealth factors.Methods: For empirical analysis, we used the data collected from the 10th Panel Study on Korean Children in 2017. Using machine learning tools such as support vector machine (SVM) and random forest (RF), we evaluated the accuracy of the model in which home environment factors classify and predict children's planning-organizing executive functions, and extract the relative importance of variables that determine these executive functions by income group.Results: First, SVM analysis shows that home environment quality and wealth factors show high accuracy in classification and prediction in all three groups. Second, RF analysis shows that estate had the highest predictive power in the high-income group, followed by income, asset, learning, reinforcement, and emotional environment. In the middle-income group, emotional environment showed the highest score, followed by estate, asset, reinforcement, and income. In the low-income group, estate showed the highest score, followed by income, asset, learning, reinforcement, and emotional environment.Conclusion: This study confirmed that home environment quality and wealth factors are significant factors in predicting children’s planning-organizing executive functions.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Predictions of Actions and Their Justifications in False-Belief Tasks: The Role of Executive Function

2014 ◽  
Vol 45 (4) ◽  
pp. 500-510 ◽  
Author(s):  
Adam Putko ◽  
Agata Złotogórska
Keyword(s):  
Working Memory ◽  
Executive Function ◽  
Language Development ◽  
Executive Control ◽  
Working Memory Capacity ◽  
Mental States ◽  
False Belief ◽  
Current Location ◽  
False Belief Tasks ◽  
Development Of Language

Abstract The main objective of this study was to examine whether children’s ability to justify their action predictions in terms of mental states is related, in a similar way as the ability to predict actions, to such aspects of executive function (EF) as executive control and working memory. An additional objective was to check whether the frequency of different types of justifications made by children in false-belief tasks is associated with aforementioned aspects of EF, as well as language. The study included 59 children aged 3-4 years. The ability to predict actions and to justify these predictions was measured with false-belief tasks. Luria’s hand-game was used to assess executive control, and the Counting and Labelling dual-task was used to assess working memory capacity. Language development was controlled using an embedded syntax test. It was found that executive control was a significant predictor of the children’s ability to justify their action predictions in terms of mental states, even when age and language were taken into account. Results also indicated a relationship between the type of justification in the false-belief task and language development. With the development of language children gradually cease to justify their action predictions in terms of current location, and they tend to construct irrelevant justifications before they begin to refer to beliefs. Data suggest that executive control, in contrast to language, is a factor which affects the development of the children’s ability to justify their action predictions only in its later phase, during a shift from irrelevant to correct justifications.

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Phum Tachachartvanich ◽  
Denis Fourches ◽  
Anatoly Soshilov ◽  
Jennifer C.Y. Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Androgen Receptor ◽  
Endocrine Disruptors ◽  
Hormone Receptors ◽  
Steroid Hormone Receptors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Polyfluoroalkyl Substances ◽  
Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

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