Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface - Asgher, Umer; Khalil, Khurram; Khan, Muhammad Jawad; Ahmad, Riaz; Butt, Shahid Ikramullah; Ayaz, Yasar; Naseer, Noman; Nazir, Salman (Frontiers in Neuroscience, 2020)
Cognitive workload is one of the widely invoked human factor in the areas of Human Machine Interaction (HMI) and Neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of brain. In this study, we have decoded four classes of mental workload (MWL) using long-short term memory (LSTM) with 89.31% average accuracy for brain-Computer Interface (BCI). The brain activity signals are acquired using functional Near-Infrared Spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system and initial classification is performed using three strong machine learning (ML) techniques, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Artificial Neural Network (ANN) with obtained average accuracies of 54.33%, 54.31%, and 69.36% respectively. In this study, novel Deep learning (DL) frameworks are proposed which utilizes Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with 87.45% and 89.31% average accuracies respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t- test and one-way F-test (ANOVA) is also performed on accuracies obtained through machine and deep learning algorithms. Results show that proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared to ML (SVM, ANN, and k-NN) algorithms.