🧠 Brain Signal Analyzer — Simulating and Analyzing Brainwave Data Using NumPy

This is a logic-heavy, NumPy-based project that simulates and analyzes brain signal data from multiple subjects over time. It focuses purely on core Python and NumPy — no visualization or external libraries — to showcase data manipulation, simulation, and analysis skills.

---

📁 Folder Structure

 ``` brain-signal-analyzer/ │ ├── 01_data_generation/ │ └── simulate_brain_data.py # Generates and saves simulated brain signal data │ ├── 02_analysis/ │ └── subject_analysis.py # Computes average and max signal values for each subject │ ├── 03_classification/ │ └── spike_detection.py # Detects abnormal spikes based on signal averages │ ├── brain_data.npy # Simulated data file (NumPy array) └── README.md # Project documentation ``` 

---

🧠 Dataset

The brain_data.npy file is a 3D NumPy array with the shape:
(5 subjects, 60 seconds, 4 signals)

Signal Types:

• delta
• theta
• alpha
• beta

---

📊 Features Implemented

✔️ Data Generation (01_data_generation)

• Simulates brainwave data using random values
• Stores the signal data as a .npy file for future use

✔️ Signal Analysis (02_analysis)

• Calculates average signal value per subject
• Calculates max signal value per subject

✔️ Spike Detection (03_classification)

• Detects and displays abnormal spikes:

  Any value greater than the subject’s average for a given signal is treated as a spike

---

🧾 Sample Output (from spike_detection.py)

Abnormal Spikes Of Subject 0 delta : [ 2 4 5 8 11 13 14 19 20 21 22 25 27 31 32 33 34 37 38 41 42 43 44 45 47 49 50 52 55 58 59]

theta : [ 0 3 4 5 8 9 10 14 15 19 20 21 22 26 30 33 35 37 39 41 45 46 48 50 52 53 54]

alpha : [ 0 3 4 5 7 10 11 17 18 26 29 30 37 38 40 41 42 44 47 49 50 52 54 55 59]

beta : [ 1 2 3 4 6 7 9 12 13 15 16 17 18 21 22 23 32 33 36 38 41 42 44 46 47 50 51 53 54 55 56 59]

---

---

🎯 Goal of the Project

This project is built to demonstrate:

• Strong logic-building using core Python
• Efficient use of NumPy arrays for real-world signal-like data
• Modular structure using folders and functions
• Clean code practices (no hardcoding, reusable logic)

---

🧠 Skills Used

• NumPy slicing, indexing, where, mean, max, random
• Python functions, loops, and structure
• Realistic simulation and detection logic

---

🚀 Future Scope (Optional Enhancements)

• Add Matplotlib bar charts for average signal visualization
• Create a GUI or dashboard to explore signal patterns
• Include CSV export for average/max/spike data

---

🚀 Tech Stack & Tags

#Python #NumPy #SignalProcessing #BrainDataAnalysis #BeginnerProject #RecruiterFriendly #LogicBuilding #MiniMajor

---

✅ Made with logic, not libraries 😉 ⭐ If you liked this project, feel free to star it or check out my GitHub for more.