English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper studies the classification problem on electroencephalogram (EEG)data of mental tasks, using standard architecture of three-layer CNN, stackedLSTM, stacked GRU. We further propose a novel classifier - a mixed LSTM modelwith a CNN decoder. A hyperparameter optimization on CNN shows validationaccuracy of 72% and testing accuracy of 62%. The stacked LSTM and GRU modelswith FFT preprocessing and downsampling on data achieve 55% and 51% testingaccuracy respectively. As for the mixed LSTM model with CNN decoder, validationaccuracy of 75% and testing accuracy of 70% are obtained. We believe the mixedmodel is more robust and accurate than both CNN and LSTM individually, by usingthe CNN layer as a decoder for following LSTM layers. The code is completed inthe framework of Pytorch and Keras. Results and code can be found athttps://github.com/theyou21/BigProject.

Mental Task Classification Using Electroencephalogram Signal