Virtual EEG: A Software-Based Electroencephalogram Designed for Undergraduate Neuroscience-Related Courses

Area of Science:

• Neuroscience
• Cognitive Science
• Educational Technology

Background:

• Investigating the brain activity-visual awareness link is crucial in neuroscience.
• Electroencephalogram (EEG) offers high temporal resolution but is challenging for undergraduate labs.
• Students often lack hands-on EEG experiment experience.

Purpose of the Study:

• Introduce Virtual EEG, a program for analyzing event-related potentials (ERPs) in undergraduate courses.
• Enable students to conduct hypothesis-driven studies on visual stimulus categorization.
• Provide a platform for students to interpret real EEG data and discover insights into visual processing.

Main Methods:

• Utilized a large database of 32-channel EEG recordings from human subjects viewing 256 visual stimuli.
• Developed a program allowing stimulus grouping, ERP graph generation, and statistical analysis of selected channels.
• Included a tutorial and assessment survey for program evaluation.

Main Results:

• Virtual EEG successfully engaged over a thousand undergraduate students at Indiana University for six years.
• Assessment results highlight the program's strengths and limitations in an educational context.
• The program facilitates student discovery in visual information processing.

Conclusions:

• Virtual EEG is an effective tool for teaching EEG data analysis and visual neuroscience concepts.
• The program bridges the gap between theoretical knowledge and practical research experience for students.
• It empowers students to engage with real neuroscience data, fostering critical thinking and research skills.