Team Project for Undergrad Final Project (Most Innovative Project Award)
The EmoEngine communicates with the Emotiv headset, receives preprocessed EEG and gyroscope data, manages user-specific or application-specific settings, performs post-processing, and translates the Emotiv detection results into an easy-to-use structure called an EmoState. This step is used to obtain the electroencephalography (EEG) signals from the neuroheadset in the form of sensor readings and store it in a file. It interfaces with the device and obtains the required sensor readings, which can be subsequently processed.
In this step, the idea is to reduce the noise in the EEG signals to some extent. The concepts of various mathematical and statistical methods will be used to reduce noise in these signals. Some advantages are: it reduces noise, lowers memory costs and algorithms become faster.
Finally, the filtered data is classified to one of the categories or classes established in the classification phase. The project employs the use a Machine Learning Algorithm to classify the data. Classification can be done using late learners or early learners approach. Late learners create the classification model only when a data element is being classified. The k-Nearest Neighbor algorithm falls into this category. Alternatively, the model can be made as soon as the training data is available. These early learner algorithms are used when the training set is relatively non-volatile. Decision trees and rule based classifiers are examples of this type
Gantt Chart for schedule
Data Flow Diagram
A plot of EEG for trials (red, blue, green) for an apple fruit when the user saw an apple while wearing neuroheadset.