Commit cec07d7f authored by Alexis Veracruz's avatar Alexis Veracruz

Update README.md

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Is EEG test-retest data consistent enough to identify an individual? Do mTBI and concussive events change the effectiveness of the algorithm due to brainwave changes?
## Abstract
Raw brainwave data taken from an electroencephalogram (EEG) system has been shown to be unique for each individual. Developing a machine learning algorithm to
Raw brainwave data taken from an electroencephalogram (EEG) system has been shown to be unique for each individual. Developing a machine learning algorithm to
identify a specific person by their raw brainwave data can help to determine if a person’s brainwaves become unidentifiable after a concussion and therefore
indicate changes in the brain.
## Tools
## Methods
### Tools
Python package to analyse EEG Data: https://martinos.org/mne/stable/index.html
## Waves in EEG
### Waves in EEG
<b> Delta:</b> 0-4 Hz
<br><b> Theta:</b> 4-8 Hz
<br><b> Alpha:</b> 8-13 Hz
<br><b> Beta:</b> 13-20 Hz
<br><b> Gamma:</b> 20-40 Hz
### <i> Subjects and Data Aquisition </i>
The 98 subjects included in the dataset were college aged males (18-24) Division I football players. Each player had a minimum
of two EEG recording sessions. The first EEG measurements were recorded before the season began (baseline), and at the end of
season (~6 months after). Some subjects had EEG retest sessions in order to record potential concussion, 20.4% of subjects indicated
concussion symptoms. At the time of each measurement, the EEG was recorded for 4-15 minutes, the subjects were in an eye-closed
resting state throughout the duration of the recording. During the recording sessions artifacts were identified and removed from
the data analysis for accuracy.
## Data
### Biosignal Recording and EEG Parameterisation
The EEG recordings were performed with electrodes secured at sites FP1, FP2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz,
P4, T6, O1, O2 with 19-channel equipment (WAVi). A headset containing the electrodes is placed on the patient. The electrodes are
examined to ensure quality contact. If contact is unacceptable, conductive gel can be added and the eSocs can be rubbed along
the scalp to exfoliate the location of the electrode in order to assist in gaining proper contact. Once contact is deemed
acceptable, a auditory P300 Eyes Closed Protocol is run. The patient is instructed to avoid any synchronized motions and blinks
during the P300 test as this will affect the quality of the data.
### Data
Data were measured in multiple times per subject, once at the start of the season, once at the end of the season and everytime the subject had a concussion.
the data is labeled alphabetically from starting from a to represent the EED data collection for different times.
**Example**
**Ex/**
if a subject has data labeled as:
......@@ -56,6 +74,21 @@ Each subject is exposed to 40 of these high pitch sounds at a random time frame.
More speficially the change of the EEG Data with regard to the high pitch sound is an amplitude increase. The amplitude increase is observed in any frequency band (depends at which frequency band the subject's EEG data is at the time of hearing the high pitch sound).
## Analysis
Feature Extraction
→data cleaning
→sessions split into epochs → 2 seconds
Do below for each epoch
-Brainwaves (delta, theta, alpha, beta, gamma)
→ channel coherence
→channel correlation Pearson
Embedding a lower dimensional manifold
-reduce dimensionality
→reduce component analysis
→Localized linear embedding (tSNE)
-Look at distances in reduced space between 2013 & 2014 baselines
### PreProccessing
* Seperate out Different waves
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