Activity Recognition in Prognostics and Health Monitoring (PHM) Related Service Environment from Electroencephalography (EEG) via Deep Learning
published: Nov. 7, 2016, recorded: August 2016, views: 1087
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
It is important to track the cognitive activity of service personnel in a Prognostics and Health Monitoring (PHM) related training or operation environment. EEG data is a good candidate for cognitive activity recognition. Analyzing EEG data in an unconstrained (natural) environment is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline and uncontrolled mixing of various tasks. This paper proposes a framework based on deep learning using both deep belief network (DBN) and deep convolutional neural network (DCNN) that monitors cognitive activity by fusing multiple non-collocated EEG probes and also selects a smaller sensor suite for a lean data collection system. Validation on realistic data along with comparison with benchmark machine learning techniques are performed. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !