Education: Bachelor of Science in electrical engineering (December 2021)
McNair Project: Using Machine Learning to Predict the “Emotions” Throughout Different Parts of the Brain (2020)
Mentor: Dave Thompson, Ph.D.
In the past few years there has been a lot of investment in biomedical engineering and technology. One of the most used research systems is the EEG cap and a more innovating term called transfer learning. The EEG cap is an electroencephalogram test that detects electrical activity in the brain using electrodes attached to the scalp. Transfer learning is the process of having one machine or model to learn a specific task from a previous model. We combined this technique to speed up the process of detecting emotions. In this research we are learning what “emotions” the user experience throughout brain. Different parts of the brain are trigger by different stimulus such as pictures, sounds, and videos. The goal is to use machine learning to predict the emotions the user will feel in different parts of the brain. Through the collection and analysis of data using the EEG cap, we predict what the user feels in specific areas of the brain. Then, by using the data collected through images we can use transfer learning to have the computer predict what the user feels when put though other stimulus such as sounds and videos. This would reduce the amount of programming and preparations to detect certain emotions produced by different stimulus. This system would prove useful in different mental therapies or just simply in trying to learn more about the human brain. This would create a new and more effective way to detect emotions using different systems and learning processes.