scholarly journals Computational testing for automated preprocessing: a matlab toolbox for better electroencephalography data processing

2016 ◽  
Author(s):  
Benjamin Cowley ◽  
Jussi Korpela ◽  
Jari Torniainen
Keyword(s):  
Method Comparison ◽  
Matlab Toolbox ◽  
Brain Functioning ◽  
Eeg Data ◽  
Computational Testing ◽  
Human Effort ◽  
Optimal Approach ◽  
High Degree ◽  
Source Of Information

EEG is a rich source of information regarding brain functioning, and is the most lightweight and affordable method of brain imaging. However, the pre-processing of EEG data is quite complicated and most existing tools present the experimenter with a large choice of methods for analysis, but no framework for method comparison to choose an optimal approach. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces excessive subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus minimise human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: i) batch processing that is easy for experts and novices alike; ii) testing and comparison of automated methods. CTAP uses the existing data structure and functions from the well-known EEGLAB tool, based on Matlab, and produces extensive quality control outputs.

scholarly journals Computational testing for automated preprocessing: a matlab toolbox for better electroencephalography data processing

2016 ◽  
Author(s):  
Benjamin Cowley ◽  
Jussi Korpela ◽  
Jari Torniainen
Keyword(s):  
Method Comparison ◽  
Matlab Toolbox ◽  
Brain Functioning ◽  
Eeg Data ◽  
Computational Testing ◽  
Human Effort ◽  
Optimal Approach ◽  
High Degree ◽  
Source Of Information

EEG is a rich source of information regarding brain functioning, and is the most lightweight and affordable method of brain imaging. However, the pre-processing of EEG data is quite complicated and most existing tools present the experimenter with a large choice of methods for analysis, but no framework for method comparison to choose an optimal approach. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces excessive subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus minimise human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: i) batch processing that is easy for experts and novices alike; ii) testing and comparison of automated methods. CTAP uses the existing data structure and functions from the well-known EEGLAB tool, based on Matlab, and produces extensive quality control outputs.

scholarly journals Computational testing for automated preprocessing: a Matlab toolbox to enable large scale electroencephalography data processing

2017 ◽  
Vol 3 ◽  
pp. e108 ◽  
Author(s):  
Benjamin U. Cowley ◽  
Jussi Korpela ◽  
Jari Torniainen
Keyword(s):  
Large Scale ◽  
Batch Processing ◽  
Matlab Toolbox ◽  
Eeg Data ◽  
Computational Testing ◽  
Human Effort ◽  
The Many ◽  
Optimal Approach ◽  
High Degree

Electroencephalography (EEG) is a rich source of information regarding brain function. However, the preprocessing of EEG data can be quite complicated, due to several factors. For example, the distinction between true neural sources and noise is indeterminate; EEG data can also be very large. The various factors create a large number of subjective decisions with consequent risk of compound error. Existing tools present the experimenter with a large choice of analysis methods. Yet it remains a challenge for the researcher to integrate methods for batch-processing of the average large datasets, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces extra subjectivity, is slow and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus reduce human effort, subjectivity and consequent error. We present the computational testing for automated preprocessing (CTAP) toolbox, to facilitate: (i) batch-processing that is easy for experts and novices alike; (ii) testing and manual comparison of preprocessing methods. CTAP extends the existing data structure and functions from the well-known EEGLAB toolbox, based on Matlab and produces extensive quality control outputs. CTAP is available under MIT licence fromhttps://github.com/bwrc/ctap.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

The challenges of diagnosing diabetes in childhood

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mairi Pucci ◽  
Marco Benati ◽  
Claudia Lo Cascio ◽  
Martina Montagnana ◽  
Giuseppe Lippi
Keyword(s):  
Diabetes Mellitus ◽  
Children And Young People ◽  
Narrative Literature ◽  
Diagnosis And Classification ◽  
Narrative Literature Review ◽  
Optimal Approach ◽  
Diagnostic Step

AbstractDiabetes is one of the most prevalent diseases worldwide, whereby type 1 diabetes mellitus (T1DM) alone involves nearly 15 million patients. Although T1DM and type 2 diabetes mellitus (T2DM) are the most common types, there are other forms of diabetes which may remain often under-diagnosed, or that can be misdiagnosed as being T1DM or T2DM. After an initial diagnostic step, the differential diagnosis among T1DM, T2DM, Maturity-Onset Diabetes of the Young (MODY) and others forms has important implication for both therapeutic and behavioral decisions. Although the criteria used for diagnosing diabetes mellitus are well defined by the guidelines of the American Diabetes Association (ADA), no clear indications are provided on the optimal approach to be followed for classifying diabetes, especially in children. In this circumstance, both routine and genetic blood test may play a pivotal role. Therefore, the purpose of this article is to provide, through a narrative literature review, some elements that may aid accurate diagnosis and classification of diabetes in children and young people.

scholarly journals Identification of the degree of impact of dams on the hydrologic regime of semi-arid rivers: an evaluation of the DHRAM method

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Naiah Caroline Rodrigues de Souza ◽  
◽  
Andrea Sousa Fontes ◽  
Lafayette Dantas da Luz ◽  
Sandra Maria Conceição Pinheiro ◽  
...  
Keyword(s):  
Hydrological Regime ◽  
Flow Regulation ◽  
Hydrologic Regime ◽  
Local Conditions ◽  
Operating Rules ◽  
Hydrologic Variability ◽  
High Degree ◽  
The Impact ◽  
Semi Arid

ABSTRACT The flow regulation that results from the implantation of dams causes consequences to the river ecosystems due to the modification on the characteristics of the hydrologic regime. The investigation of these changes become relevant, mainly in semi-arid regions where there is a great amount of these hydraulic structures and lack of such analyzes. Considering the above, this paper aims to evaluate the Dundee Hydrological Regime Alteration Method (DHRAM) through the classification of the degree of impact of dams located on rivers Itapicuru, Paraguaçu and their tributaries, verifying the adequacy of its use to represent the semi-arid hydrologic regime. Thereby, the DHRAM was applied in three versions: considering the thresholds that define the scores to classify the degree of impact in its original set (accordingly to Black et al. (2005)); with the adjustment of those thresholds to local conditions; and, with the regrouping of variables and adjustment of thresholds. The results showed that the method in its original set is applicable to semi-arid rivers, however it tends to be very restrictive against the high natural hydrologic variability characteristic of these rivers, and it ends up pointing to a high degree of alteration for dams that are known for not causing a very siginifcant flow regulation. The DHRAM with the regrouping of variables and the adjustment of thresholds presented the classification that approached the most to the known characteristics of the studied dams, being useful for the evaluation of the impact of dams still in project, and also to guide the adoption of operating rules that minimize the most significant hydrologic alterations that are identified.

Evaluational Reactions to Erotic Literature

1965 ◽  
Vol 16 (3) ◽  
pp. 985-994 ◽  
Author(s):  
Leon A. Jakobovits
Keyword(s):  
Short Stories ◽  
Hard Core ◽  
Cumulative Effect ◽  
Sampling Bias ◽  
Male And Female ◽  
Warm Up ◽  
Erotic Literature ◽  
High Degree ◽  
Very High

On the basis of analysis of a variety of erotic literature Kronhausen and Kronhausen (1959) have suggested that there seem to be two general types: erotic realism ( ER) and hard-core obscenity ( O). Using three of the distinguishing criteria which were identified (context, exaggeration, and anti-eroticism), 20 short stories were specifically written in such a way that 10 had the characteristics of ER and the others had the characteristics of O. Study I showed a very high degree of agreement between judges in their classification of these stories as either ER or O. Study II revealed that male and female readers react differentially to the two types of stories. Females consistently rate O as more interesting and sexually stimulating than males do, the latter finding ER as more arousing than O. Other evaluational reactions are also described. A “warm-up” cumulative effect with successive reading was found with both sexes. The possibility of sampling bias affecting the data was noted.

scholarly journals Assessment of genetic diversity in Indian Barnyard millet (Echinochloa spp. complex) using morphological and molecular markers

2016 ◽  
Vol 8 (3) ◽  
pp. 1643-1648 ◽  
Author(s):  
M. P. Moharil ◽  
Dipti Gawai ◽  
N. Dikshit ◽  
M.S. Dudhare ◽  
P. V. Jadhav
Keyword(s):  
Genetic Diversity ◽  
Molecular Markers ◽  
Genetic Improvement ◽  
Molecular Diversity ◽  
Genetic Relationships ◽  
Barnyard Millet ◽  
Clear Cut ◽  
Cultivated Species ◽  
High Degree

In the present study, morphological and molecular markers (RAPD primers) were used to analyze the genetic diversity and genetic relationships among 21 accessions of Echinochloa spp. complex comprising the wild and cultivated species collected from Melghat and adjoining regions of Vidarbha, Maharashtra. The availability of diverse genetic resources is a prerequisite for genetic improvement of any crop including barnyard millet. A high degree of molecular diversity among the landraces was detected. Among the 21 genotypes, two major groups (A and B) were formed, at 67.28 % similarity, which clearly encompasses 15 accessions of E. frumentacea and 6 accessions of E. colona. Higher similarity was observed in accessions of E. frumentacea. The accessions IC 597322 and IC 597323 also IC 597302 and IC 597304 showed more than 94% similarity among themselves. The classification of genetic diversity has enabled clear-cut grouping of barnyard millet accessions into two morphological races (E. frumentacea and E. colona).

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

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