1.	Introduction
2.	Project Overview
3.	System Architecture
4.	Implementation Steps
5.	Code Modules
•	Data Ingestion Module
•	Data Preprocessing Module
•	Feature Extraction Module
•	Correlation Analysis Module
•	Visualization Module
6.	Dependencies and Installation
7.	Running the Application
8.	Conclusion
9.	Appendices
•	Appendix A: Full Source Code
•	Appendix B: Dataset Format Examples

•	Eye tracking data
•	Face heat map tracking
•	Heart rate & blood pressure readings
•	EEG data
•	Self-response questionnaire surveys

1.	Data Ingestion: Reads and validates data from various sources.
2.	Data Preprocessing: Cleans and formats data for analysis.
3.	Feature Extraction: Extracts meaningful features from raw data.
4.	Correlation Analysis: Identifies relationships between different data types.
5.	Visualization: Generates graphs and heatmaps to illustrate findings.

•	Input Layer: Handles data ingestion from multiple sources.
•	Processing Layer: Performs preprocessing and feature extraction.
•	Analysis Layer: Conducts statistical and machine learning analyses.
•	Output Layer: Generates visualizations and reports.

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

pip install numpy pandas scipy scikit-learn matplotlib seaborn mne

project_root/
│
├── data/
│   ├── eye_tracking/
│   ├── face_heatmap/
│   ├── vitals/
│   ├── eeg/
│   └── surveys/
│
├── src/
│   ├── data_ingestion.py
│   ├── data_preprocessing.py
│   ├── feature_extraction.py
│   ├── correlation_analysis.py
│   └── visualization.py
│
├── main.py
└── requirements.txt

•	Purpose: Load data from various formats (CSV, JSON, EDF for EEG).
•	Implement functions to read each data type.

•	Clean data (handle missing values, normalize).
•	Synchronize data based on timestamps.

•	Extract features like fixation durations from eye tracking.
•	Compute statistical measures from EEG (e.g., band power).
•	Derive scores from survey responses.

•	Use statistical methods (Pearson, Spearman correlations).
•	Implement machine learning models if necessary.

•	Create plots to visualize data distributions.
•	Generate heatmaps for correlation matrices.

•	Coordinate the workflow:
•	Ingest data.
•	Preprocess data.
•	Extract features.
•	Perform analysis.
•	Visualize results.

•	Run the application with sample data.
•	Use logging to track the application’s execution.
•	Debug any issues that arise.

•	Write docstrings for all functions and classes.
•	Create a README file with instructions.

import pandas as pd
import mne  # For EEG data

def load_eye_tracking_data(path):
df = pd.read_csv(path)
return df

def load_face_heatmap_data(path):
df = pd.read_csv(path)
return df

def load_vitals_data(path):
df = pd.read_csv(path)
return df

def load_eeg_data(path):
raw = mne.io.read_raw_edf(path, preload=True)
return raw

def load_survey_data(path):
df = pd.read_csv(path)
return df

import pandas as pd

def preprocess_eye_tracking(df):
df.fillna(method='ffill', inplace=True)
# Additional preprocessing steps
return df

def preprocess_eeg(raw):
raw.filter(1., 50.)  # Bandpass filter
# Additional preprocessing steps
return raw

def synchronize_data(dfs, on='timestamp'):
from functools import reduce
df_merged = reduce(lambda left, right: pd.merge(left, right, on=on), dfs)
return df_merged

import numpy as np

def extract_eye_tracking_features(df):
    features = {}
    features['avg_fixation_duration'] = df['fixation_duration'].mean()
    # Additional features
    return features

    def extract_eeg_features(raw):
    epochs = mne.make_fixed_length_epochs(raw, duration=2.0)
    psd, freqs = mne.time_frequency.psd_welch(epochs)
    features = psd.mean(axis=0)
    return features

import pandas as pd

def compute_correlations(features_df):
corr_matrix = features_df.corr(method='pearson')
return corr_matrix

def identify_significant_correlations(corr_matrix, threshold=0.5):
significant = corr_matrix[(corr_matrix > threshold) & (corr_matrix != 1.0)]
return significant

import seaborn as sns
import matplotlib.pyplot as plt

def plot_correlation_heatmap(corr_matrix):
sns.heatmap(corr_matrix, annot=True, fmt=".2f")
plt.show()

def plot_feature_distributions(features_df):
features_df.hist(bins=15, figsize=(15, 10))
plt.show()

numpy
pandas
scipy
scikit-learn
matplotlib
seaborn
mne

pip install -r requirements.txt

1.	Prepare the Data
•	Place your datasets in the respective folders within the data/ directory.
•	Ensure all data files are properly formatted (see Appendix B).
2.	Configure main.py

from src.data_ingestion import *
from src.data_preprocessing import *
from src.feature_extraction import *
from src.correlation_analysis import *
from src.visualization import *

def main():
# Data Ingestion
eye_data = load_eye_tracking_data('data/eye_tracking/eye_data.csv')
eeg_data = load_eeg_data('data/eeg/eeg_data.edf')
survey_data = load_survey_data('data/surveys/survey_data.csv')

eye_data = preprocess_eye_tracking(eye_data)
eeg_data = preprocess_eeg(eeg_data)

eye_features = extract_eye_tracking_features(eye_data)
eeg_features = extract_eeg_features(eeg_data)
survey_features = survey_data.mean().to_dict()

features_df = pd.DataFrame([eye_features, eeg_features, survey_features])

corr_matrix = compute_correlations(features_df)
significant_corrs = identify_significant_correlations(corr_matrix)

plot_correlation_heatmap(corr_matrix)
plot_feature_distributions(features_df)

if __name__ == '__main__':
main()

Appendix A: Full Source Code

Appendix B: Dataset Format Examples

timestamp	fixation_duration	saccade_amplitude	pupil_size
1609459200	0.2	1.5	3.1
1609459201	0.15	1.2	3.0

•	Standard EDF file with properly labeled channels.
•	Sampling rate: 256 Hz.

question_id	response_score
1	4
2	3