Abstract
Magnetoencephalography (MEG) is a non-invasive imaging technique that measures the naturally occurring electrical activity of the brain. A MEG signal contains important information about the health of the brain and can be used to detect any abnormalities that could point to a neurological disease. MEG sensors are very sensitive, and so they are very susceptible to noise. Denoising these signals efficiently will make analyzing the data much easier. In this paper, we have utilized several components in order to obtain, denoise, and then store MEG data. First, data is submitted into a React application which then stores the raw data, along with user information into a MYSQL database. Then, the data passes through a 9-layer Denoising Autoencoder (DAE). Afterwards the output is then stored in a separate MYSQL database and its noisy version. The SNR of a signal after passing it through the model was able to be increased by a maximum of 88%. On average, the model was able to increase the SNR by 45.63%. Besides providing neurologists valuable information regarding the brain, it also serves as an easily accessible tool for viewing and cleaning MEG data.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
