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+## About Dataset
+
+### Context
+This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.
+
+
+### Attribute Information:
+The original dataset from the reference consists of 5 different folders, each with 100 files, with each file representing a single subject/person. Each file is a recording of brain activity for 23.6 seconds. The corresponding time-series is sampled into 4097 data points. Each data point is the value of the EEG recording at a different point in time. So we have total 500 individuals with each has 4097 data points for 23.5 seconds.
+
+We divided and shuffled every 4097 data points into 23 chunks, each chunk contains 178 data points for 1 second, and each data point is the value of the EEG recording at a different point in time. So now we have 23 x 500 = 11500 pieces of information(row), each information contains 178 data points for 1 second(column), the last column represents the label y {1,2,3,4,5}.
+
+The response variable is y in column 179, the Explanatory variables X1, X2, …, X178
+
+y contains the category of the 178-dimensional input vector. Specifically y in {1, 2, 3, 4, 5}:
+
+5 - eyes open, means when they were recording the EEG signal of the brain the patient had their eyes open
+
+4 - eyes closed, means when they were recording the EEG signal the patient had their eyes closed
+
+3 - Yes they identify where the region of the tumor was in the brain and recording the EEG activity from the healthy brain area
+
+2 - They recorder the EEG from the area where the tumor was located
+
+1 - Recording of seizure activity
+
+All subjects falling in classes 2, 3, 4, and 5 are subjects who did not have epileptic seizure. Only subjects in class 1 have epileptic seizure. Our motivation for creating this version of the data was to simplify access to the data via the creation of a .csv version of it. Although there are 5 classes most authors have done binary classification, namely class 1 (Epileptic seizure) against the rest.
+
+This Dataset collect from UCI Machine Learning Repository
+
+### Acknowledgements
+Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907
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