AlgoLYNXathon Team A repo
Details of the hackathon can be found here
The repo contain three notebooks
eyes-eeg.ipynb
which details how data can be downloaded from Open Neuro pagearshy_preparedata_final.ipynb
which details howTeam A
preprocessed the data to be used in classificationarshy_approach2_2labelclassification.ipynb
which goes through how Team built aeyes open
/eyes closed
classification usingRandom Forest Classifier
Preprocessing Approaches
The different preproces we used
filter
to filter the signals between desired Hzresample
to resample the eeg signal from acqusition frequency to a desired frequencyICA
to removeecg, eog
related artifactsfized length epochs
to break the continuous signal to number of samples
The following approaches were used to preproces that data
Approach 1
- filter between 1 and 40
- resample from 500hz to 100 hz
- remove artifacts based on ICA
- epoch 50s
Approach 2
- filter between 1 and 40
- resample from 500hz to 100 hz
- remove artifacts based on ICA
- epoch 50s and average
Approach 3
- filter between 1 and 20
- resample from 500hz to 100 hz
- remove artifacts based on ICA on epochs
- epoch 50s
Approach 4
- filter between 1 and 20
- resample from 500hz to 100 hz
- remove artifacts based on ICA on epochs
- epoch 50s and average
Classification Methods
Method 1
- only the occipital channels were used in classification [
PO3
,PO7
,Oz
,O1
,POz
,PO4
,PO8
,O2
] RandomForestClassifier
was used as the classifier of choice
Method 2
- only the Oz channel was used in classification
RandomForestClassifier
was used as the classifier of choice
Method 3
- Following 10 channels were used [
PO3
,PO7
,Oz
,O1
,POz
,PO4
,PO8
,O2
,P3
,P4
] RandomForestClassifier
was used as the classifier of choice