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.

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.

The Uses of Association: Hands and Feasts in Beowulf

PMLA ◽  
1963 ◽  
Vol 78 (1) ◽  
pp. 8-14 ◽  
Author(s):  
James L. Rosier
Keyword(s):  
Old English ◽  
Word Formation ◽  
Close Attention ◽  
Critical Studies ◽  
Specific Effects ◽  
Bona Fide ◽  
The Subject ◽  
The Many ◽  
High Degree

The Beowulf Poet's extraordinary facility in using a vast and diverse word-hoard has long excited students of the poem. Among the critical studies, discussions of vocabulary rank high in number, and almost every conceivable approach to the subject has been investigated either in part or with a high degree of thoroughness. Single words, such as ealuscerwen, and groups of related words, such as rime-words, kennings, and words of Christian content or reference, have received close attention, as well as larger lexical patterns, such as variation and the formulaic texture, while further studies have compared the vocabulary with that of other Old English poems or Nordic literatures. Aside from purely lexicographical or etymological inquiries, there are three perspectives to which these many discussions generally belong: 1) descriptive: usually statistical observations about the number of compounds relative to simplices or of formulas relative to the whole vocabulary of the poem, or a comparison of the frequency of certain lexical types with other poems, or a classification of the habits of word-formation; 2) figurative and appellative: the types of verbal figures and their analogues elsewhere in Old English and Old Norse; and 3) usage: the use of words in particular contexts or for specific effects, and the structural use of synonymic substitution and variation. The first emphasis is important because it reveals the composition and its formative strata of the poem's total vocabulary, and also the lexical relationships with other poetry or poetic traditions. The second serves to isolate a lexical stratum which is by nature exclusively poetic and to observe how much of this stratum is probably original and how much traditional. But it is the third perspective which is interested most essentially in the poet, since here the attempt is made to discern the many ways by which he has used language significantly to dramatize, emphasize, elucidate, intimate, and so on. Much that has been written in this category has concerned itself with the larger patterns of variation as a characterizing, describing, or structural device, rather than with smaller, more confined, strokes of verbal association and verbal play. A well-known instance of the latter is the epithet for Grendel, healoegn (142) which, in its context, wherein a bona fide hall-thane anxiously seeks out a hiding place as protection against the intruder, may with complete justification be termed ironic, and the same thing may be said of a similar appellation used later for both Grendel and Beowulf, renweardas (770), There are also hints here and there that the poet may have been influenced by learned Latin figures. Many years ago Albert Cook compared flod blode weol (1422; Exodus 463, flod blod gewod) to Aldhelm's fluenta cruenta (De Virginitate, 2600), and more recently H. D. Meritt called attention to the similarity between Hrothgar's warning that in death “eagena bearhtm / forsiteo ond forsworces” (1766b-67a) and Aldhelm's “ferreus leti somnus palpebrarum conuolatus non tricaverit” (De Virg. Prose, 321.7, ed. Ewald). It is in the smaller strokes, I think, that the poet's acumen and craft are most incisively contained, and it is to some of these that the present discussion is devoted.

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.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

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