Week 8 (11/16/2020)

Figure 1: Example 32 Channel Dataset From EEGLAB

Figure 1 illustrates 32 channel sample dataset that was provided and tested with EEGLAB. After testing multiple repositories on GitHub, the team decided that using software that utilizes MATLAB would be a better fit tool for this capstone. UCSD developed EEGLAB to process continuous and event-related EEG data. EEGLAB supports multiple files and has a large array of documentation to instruct those interested in using their tool. Currently, the team is performing research on uploading other datasets into EEGLAB. EEGLAB supports different types of datasets to be used. The following link provides sufficient information on publicly available EEG datasets that have been collected by various universities throughout the world: https://github.com/meagmohit/EEG-Datasets

The team met on Tuesday with Dr. Hossein Asghari to discuss the hardware that will be purchased to begin building the EEG helmet. The items will be arriving within a week after being ordered. The list of items to be purchased are:

• Microprocessor

• Transmitter

• Electrodes

• Headband

THIS WEEK

Tyler

• Completed list of material required for purchase

• Reviewed circuit diagrams of existing EEG helmets

Mark

• Submitted IRB documents to Loyola Marymount University

• Reviewed and worked through the documentation of EEGLAB

• Started state diagram for machine learning algorithm that will be applied to the EEG helmet
  

NEXT WEEK

Tyler

• Further review the electronics components that will be used to build the EEG helmet

Mark

• Test EEGLAB with different datasets and edit some of the datasets to see the difference