Quantification of Mental Workload Using a Cascaded Deep One-dimensional
Convolution Neural Network and Bi-directional Long Short-Term Memory
Model
Abstract
In this paper, a new cascade one-dimensional convolution neural network
(1DCNN) and bidirectional long short-term memory (BLSTM) model has been
developed for binary and ternary classification of mental workload
(MWL). MWL assessment is important to increase the safety and efficiency
in Brain-Computer Interface (BCI) systems and professions where
multi-tasking is required. Keeping in mind the necessity of MWL
assessment, a two-fold study is presented, firstly binary classification
is done to classify MWL into Low and High classes. Secondly, ternary
classification is applied to classify MWL into Low, Moderate, and High
classes. The cascaded 1DCNN-BLSTM deep learning architecture has been
developed and tested over the Simultaneous task EEG workload (STEW)
dataset. Unlike recent research in MWL, handcrafted feature extraction
and engineering are not done, rather end-to-end deep learning is used
over 14 channel EEG signals for classification. Accuracies exceeding the
previous state-of-the-art studies have been obtained. In binary and
ternary classification accuracies of 96.77% and 95.36% have been
achieved with 7-fold cross validation, respectively.