Event Title

Identifying Symptoms of Depression Using Machine Learning & Alleviation via Virtual Environment.

Mentor 1

Veysi Malkoc

Start Date

16-4-2021 12:00 AM

Description

The EEG or electroencephalograph is a method of monitoring the electrical activity of an individual’s brain by placing electrodes on the patient's scalp and is currently being used in cognitive psychological research. This study plans to utilize the EEG activity and send those waveforms to code that we will develop to determine whether a patient has symptoms of depression. Before this occurs we will utilize a virtual reality headset and a program called Unity to create multiple virtual environments that may have the chance to temporarily alleviate those symptoms of depression. By creating these environments in virtual reality, we can convince the mind that the body is somewhere else and by creating an environment that can be interacted with by the user, it can bolster these effects and create emotions of happiness and comfort. The EEG’s normally noninvasive nature is appealing to its patients and is beneficial to us because we can continue to monitor the waveforms while the patient is in the virtual reality environment; furthermore, our hope for this study is that we can successfully identify symptoms of depression using machine learning and temporarily alleviate those symptoms by placing the user in a virtual environment. This work is important because it opens the door for the development of non-prescription methods of symptom alleviation; moreover, this method compared to prescription medications could be more cost-effective and accessible to those that suffer from these symptoms.

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Apr 16th, 12:00 AM

Identifying Symptoms of Depression Using Machine Learning & Alleviation via Virtual Environment.

The EEG or electroencephalograph is a method of monitoring the electrical activity of an individual’s brain by placing electrodes on the patient's scalp and is currently being used in cognitive psychological research. This study plans to utilize the EEG activity and send those waveforms to code that we will develop to determine whether a patient has symptoms of depression. Before this occurs we will utilize a virtual reality headset and a program called Unity to create multiple virtual environments that may have the chance to temporarily alleviate those symptoms of depression. By creating these environments in virtual reality, we can convince the mind that the body is somewhere else and by creating an environment that can be interacted with by the user, it can bolster these effects and create emotions of happiness and comfort. The EEG’s normally noninvasive nature is appealing to its patients and is beneficial to us because we can continue to monitor the waveforms while the patient is in the virtual reality environment; furthermore, our hope for this study is that we can successfully identify symptoms of depression using machine learning and temporarily alleviate those symptoms by placing the user in a virtual environment. This work is important because it opens the door for the development of non-prescription methods of symptom alleviation; moreover, this method compared to prescription medications could be more cost-effective and accessible to those that suffer from these symptoms.