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eeg-eye-state

eeg-eye-state

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Author: Oliver Roesler, it12148'@'lehre.dhbw-stuttgart.de Source: [UCI](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State), Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany Please cite: All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data.

15 features

Class (target)nominal2 unique values
0 missing
V1numeric548 unique values
0 missing
V2numeric452 unique values
0 missing
V3numeric345 unique values
0 missing
V4numeric312 unique values
0 missing
V5numeric285 unique values
0 missing
V6numeric330 unique values
0 missing
V7numeric290 unique values
0 missing
V8numeric294 unique values
0 missing
V9numeric304 unique values
0 missing
V10numeric346 unique values
0 missing
V11numeric419 unique values
0 missing
V12numeric343 unique values
0 missing
V13numeric558 unique values
0 missing
V14numeric592 unique values
0 missing

107 properties

14980
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
14
Number of numeric attributes.
1
Number of nominal attributes.
3343.82
Third quartile of standard deviation of attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4316.88
Mean of means among attributes of the numeric type.
0.46
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2713.56
First quartile of kurtosis among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.23
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4193.08
First quartile of means among attributes of the numeric type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
22.48
First quartile of skewness among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.23
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
71.74
Mean skewness among attributes of the numeric type.
37.98
First quartile of standard deviation of attributes of the numeric type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.12
Percentage of instances belonging to the most frequent class.
1767.29
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
8257
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
9352.7
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
2056.52
Minimum kurtosis among attributes of the numeric type.
4271.63
Second quartile (Median) of means among attributes of the numeric type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14979.18
Maximum kurtosis among attributes of the numeric type.
4009.77
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
84.61
Second quartile (Median) of skewness among attributes of the numeric type.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
4644.02
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
627.16
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
6.67
Percentage of binary attributes.
Third quartile of entropy among attributes.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-13.62
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
14971.65
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
122.39
Maximum skewness among attributes of the numeric type.
29.29
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
4466.13
Third quartile of means among attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5891.29
Maximum standard deviation of attributes of the numeric type.
44.88
Percentage of instances belonging to the least frequent class.
93.33
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
6723
Number of instances belonging to the least frequent class.
6.67
Percentage of nominal attributes.
122.34
Third quartile of skewness among attributes of the numeric type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
8904.72
Mean kurtosis among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.

11 tasks

615 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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