EEG-ECG-EMG-Analysis

All Data are extracted from OpenBCI Kit

Visualization of signal in all channels
Statistical reports (mean, std, variance, correlation..etc)
Power spectrum and data filtering with high pass filter
Detection of most notable frequencies in the signal
Model in SVM and Random Forest to classify the signal
Time Series Analysis (TODO)
Topological map
Correlation Between EEG and EMG
Multiple Linear Regression EEG vs ECG and EMG
Added Analysis for other samples

Model and Signal Classification

In this section, we just apply the the following algorithms on the data.

in order to classify the signal whether it belongs to rest or activity

Common setup

test_size = 0.5

SVM

C=10E3
kernel = linear

confusion_matrix

	positive	negative
positive	4	0
negative	0	2

classification_report

	precision	recall	f1-score	support
0	1.00	1.00	1.00	4
1	1.00	1.00	1.00	2
accuracy	1.00	6
macro avg	1.00	1.00	1.00	6
weighted avg	1.00	1.00	1.00	6

accuracy_score

1.0

Random Forests

random_state = 0
iterations = 100

confusion_matrix

	positive	negative
positive	3	1
negative	0	2

classification_report

	precision	recall	f1-score	support
0	1.00	0.75	0.86	4
1	0.67	1.00	0.80	2
accuracy	0.83	6
macro avg	0.83	0.88	0.83	6
weighted avg	0.89	0.83	0.84	6

accuracy_score

0.8333333333333334

Rest Data

Data Loading

The data is first transformed into suitable format using a converter code,
to be in csv format and remove redunant columns

Data Visualization

We Visualize the time series in time domain. to make a visual check that we are loading the correct data,

and we can use it to the next steps

Data Statistical Reporting

Here we generate a full report for each sample,

to see the correlation between signals and also the see the Statistical features of the data itself

Transformation of the dataset to frequency domain and generation of power specturm

We do this, because EEG is only useful in frequency, and it’s not having useful features in time domain alone,
also to apply a HPF to choose the relevant features

Building the final dataset for classifcation

We combine the data from all the samples, and add relevant features, to build a small dataset for the model itself

Step Data