Zero Base Training Report

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Other studies have processed EEG data by using different classification methods in the zero base training report, different classification parameters in order to rate the effectiveness of electrode relevancy. In my experiment I used linear discriminant analysis and focused on the 02 electrode in order to classifier event-related potentials from the eeg data. In this project the experiment model was the most effective way to test this hypothesis and create the system with the limited resources. But their is always better ways to preprocess the data and optimize the filters. The experiment only gave the difference in data result and event related potential (ERP) differences. If a system that looked at these parameters and looked at more parameters…show more content…
More specifically, we sought to use brain activity measurements of ERPs to reconstruct images. A recent study used functional magnetic resonance imaging (fMRI) to measure brain activity in visual cortex as a person looked at several hours of movies. We then used these data to develop computational models that could predict the pattern of brain activity that would be elicited by any arbitrary movies (i.e., movies that were not in the initial set used to build the model). We used EEG data to measure brain activity during stimulus. Then created a system for preprocessing the data to fine tune the data to recognise image stimulus ERPs.
In the future of this study we will be able to create an intuitive way of image reconstruction. As you move through the world or you look at pictures, a dynamic, ever-changing pattern of activity is evoked in the brain. The goal of the mind image identification system was to create a more fine tuned way to read and related image evoked data. To do this, we create encoding models that describe how images are transformed into brain activity, and then we use those models to decode brain activity and reconstruct the
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