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Polygence Scholar2023
Eashan Gani's profile

Eashan Gani

Class of 2026Ooltewah, Tennessee


Hello! My name is Eashan Gani and my project is on how the multi-channel EEG recordings with the Discrete Wavelte Transform enhance EEG signal processing. I chose to work on this project as I am willing to learn more in depth of the Electroencephalogram(EEG) and the various techniques in reading brain signals. After completing my project, I am hoping to publish my research paper to a journal and would like to present my research in a competition or during a conference in the Polygence symposium.


  • "The Implementations of the Discrete Wavelet Transform in EEG Signal Processing" with mentor David (Nov. 11, 2023)

Eashan's Symposium Presentation

Project Portfolio

The Implementations of the Discrete Wavelet Transform in EEG Signal Processing

Started May 9, 2023

Abstract or project description

The electroencephalogram (EEG) is a tool that measures the brain’s electrical signals and helps to detect epileptic seizures that shape the electrical activity of the brain. The EEG records abnormalities in brain waves or in the electrical activity in the brain and information collected by the EEG is later depicted using a computer-based system known as the brain-computer interface (BCI). The brain-computer interface analyzes brain signals and translates signals into commands that are displayed on an output device. As the BCI is a communication pathway between the brain and an output device, there are steps that are taken from the BCI to ensure that commands are portrayed from the brain’s electrical activity to the output device. Currently, the most used method during EEG signal signal processing is the fast fourier transform (FFT), which converts digital signals of the time domain into the frequency domain, but attains a restricted range of waveform data and analyzes signals that are continuous. In order to counteract the issue of continuous EEG signals, some scientists started using the discrete wavelet transform technique (DWT), which projects highly detailed images of brain signals and measures the amount of energy that is contained within specific frequency bands in a signal, helping the BCI achieve signal processing from EEG signals in a more efficient way compared to the FFT. Furthermore, the DWT considers reducing noise levels in EEG signals by compressing EEG data, which also helps improve the signal-to-noise ratio during the collection of brain signals via the EEG. With the discrete wavelet transform, scientists use multi-channels (electrodes that capture brainwave activity) in order to avoid the loss of crucial information and specify the data collected by the EEG. Unlike the FFT, the DWT takes into account examining large spikes of data and plays a crucial role in providing localization in the time and frequency domain. This research paper aims to analyze how the usage of the discrete wavelet transform along with multi-channel EEG effectively strengthens signal processing from EEG signals compared to the fast fourier transform.