Optimizing EEG Visualization Through Remote Data Retrieval

A common practice in analyzing brain activity is capturing electroencephalograms (EEG), taken by place electrodes on the patient's scalp which measure electrical activity via voltage differences. These multichannel voltage signals are commonly stored in the European Data Format (EDF). An EDF file of an unpruned EEG can become quite large -- 1MB of an EDF file translates to roughly 1 minute of an EEG recording session with a sampling rate of 250Hz. Additionally, full datasets from the TUH EEG Corpus [1] can reach sizes of over 800GB. Because of the size of the EEG recordings, there is a need for efficient visualization and analysis tools. In this work, we focus on enhancing the user experience by streamlining the process of retrieving EDF data from our server.