During my internship at Makonis Software Solutions, I played a key role in developing a brainwave cleaning algorithm using the Fast Fourier Transform (FFT) technique. This project involved:

• Understanding Brainwaves: I gained a solid understanding of different brainwave frequencies (delta, theta, alpha, beta, gamma) and their associated mental states (sleep, relaxation, focus, etc.)
• FFT Implementation: I implemented the FFT algorithm in Python to analyze and filter electroencephalography (EEG) data. This allowed us to identify and remove unwanted noise from the signal, such as power line interference or muscle activity.
• Data Acquisition and Processing: I explored using Arduino for data acquisition from EEG sensors. This involved interfacing the Arduino with the Python code to capture real-time brainwave data for analysis and cleaning.
• Potential Applications: I researched the potential applications of this brainwave cleaning algorithm in various fields, such as:
  • Neurofeedback: To help users achieve specific brainwave states for relaxation, focus enhancement, or even pain management.
  • Brain-Computer Interfaces (BCIs): To improve the signal quality for more accurate control of external devices using brainwaves.
  • Neuroscience Research: To enhance the analysis of brain activity in research studies.

Technical Skills:

• Python Programming
• Fast Fourier Transform (FFT)
• EEG Data Analysis
• Arduino Interfacing (if applicable)

Outcomes:

• We have developed a functional brainwave cleaning algorithm using Python and FFT.
• Gained experience in working with EEG data and its applications.
• Enhanced understanding of brainwave activity and its potential for various technological advancements.