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Eeg eye state

machine-learning

Files Size Format Created Updated License Source
3 3MB arff csv zip 1 year ago 9 months ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at https://www.openml.org/d/1471 Author: Oliver Roesler Source: UCI, Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany Please cite: UCI All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
eeg-eye-state_arff 2MB arff (2MB)
eeg-eye-state 2MB csv (2MB) , json (3MB)
eeg-eye-state_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 2MB zip (2MB)

eeg-eye-state_arff  

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This is a preview version. There might be more data in the original version.

eeg-eye-state  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
V1 1 number (default)
V2 2 number (default)
V3 3 number (default)
V4 4 number (default)
V5 5 number (default)
V6 6 number (default)
V7 7 number (default)
V8 8 number (default)
V9 9 number (default)
V10 10 number (default)
V11 11 number (default)
V12 12 number (default)
V13 13 number (default)
V14 14 number (default)
Class 15 number (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/machine-learning/eeg-eye-state
data info machine-learning/eeg-eye-state
tree machine-learning/eeg-eye-state
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/eeg-eye-state/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/eeg-eye-state/r/0.arff

curl -L https://datahub.io/machine-learning/eeg-eye-state/r/1.csv

curl -L https://datahub.io/machine-learning/eeg-eye-state/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/machine-learning/eeg-eye-state/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/machine-learning/eeg-eye-state/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/machine-learning/eeg-eye-state/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/machine-learning/eeg-eye-state/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

The resources for this dataset can be found at https://www.openml.org/d/1471

Author: Oliver Roesler
Source: UCI, Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany
Please cite: UCI

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 analyzing 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.

The features correspond to 14 EEG measurements from the headset, originally labeled AF3, F7, F3, FC5, T7, P, O1, O2, P8, T8, FC6, F4, F8, AF4, in that order.

Datapackage.json

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