@@ -53,31 +53,20 @@ Same dimensions as the raw data. Gives a coloring to the EEG data, that shows ho

* 19 columns, ~61440 rows (256 s time step total of 4 minute data)

## Analysis

1. Extract (𝜶, 𝛽, 𝛾,𝜃) Wavefo

1. Extract (𝜶, 𝛽, 𝛾,𝜃) Waveform

2. Divide into 2s epochs

3.

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

## Results

### PreProccessing

* Seperate out Different waves

* Features extraction using neural network

* FFT : static

* Short-Time FFT : FFT with window

* Wavelet

3. Features extraction using neural netork

4. FFT: static

5. Short-Time FFT: FFR with window

6. Wavelet

3. Computer static features for each epoch

4. Channel coherence (each wave)

5. Channel correlation Pearson (each wave)

6. Embedding a lower dimensional manifold

7. Reduce dimensionality

8. Reduce component analysis

9. Localized linear embedding (tSNE)

10. Analyse distances in reduced space between 2013 & 2014 baselines

### Clustering

...

...

@@ -89,6 +78,9 @@ Embedding a lower dimensional manifold

* Dynamics Time warping (Distance Meausure)

### Prediction Model

## Results

The data did not show siginificant differences in the channel cohernece in alpha frequency between concussed and non concussed subjects. Additional features need

to be analyzed in order to determine if EEG is a reliable identifier for a subject with mTBI.