Accession Number : AD1044887


Title :   A Report on Applying EEGnet to Discriminate Human State Effects on Task Performance


Descriptive Note : Technical Report,01 Jun 2017,30 Sep 2017


Corporate Author : US Army Research Laboratory Aberdeen Proving Ground United States


Personal Author(s) : Gauff,Ashton ; Munoz-Barona,Humberto ; Bohannon,Addison ; Vettel,Jean


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1044887.pdf


Report Date : 01 Jan 2018


Pagination or Media Count : 22


Abstract : In this project, we utilized optimization to discriminate brain data. Participants completed 2 cognitive tasks while ongoing brain activity was recorded from electrodes on their scalp. Our analysis examined whether we could identify what task the participant was performing from differences in the recorded brain time series. We modeled the relationship between input data (brain time series) and output labels (task A and task B) as an unknown function, and we found an optimal approximation of that function from among a family of functions. We employed stochastic gradient descent to minimize the estimation error known as the loss function. The optimal function from among our family of approximate functions, EEGNet, successfully discriminated brain data from a single participant with approximately 90% accuracy. Future research will apply EEGNet on data from more participants as well as develop approaches to adapt its architecture for the non-Euclidean domains.


Descriptors :   MACHINE LEARNING , deep learning , ELECTROENCEPHALOGRAPHY , brain , cognition , electrodes , TIME SERIES ANALYSIS , smart technology , algorithms , information processing , image processing , AUTOMATED SPEECH RECOGNITION


Subject Categories : Cybernetics
      Medicine and Medical Research


Distribution Statement : APPROVED FOR PUBLIC RELEASE