“The use of functional near-infrared spectroscopy in neuroergonomics,” in Neuroergonomics, ed. Elsevier: Academic Press.Īyaz H., Izzetoglu M., Izzetoglu K., Onaral B. Neuroergonomics: The Brain at Work and Everyday Life. Assessment and classification of mental workload in the prefrontal cortex (PFC) using fixed-value modified beer-lambert law. Neural network classification of brain hemodynamic responses from four mental tasks. Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms. Nsight Systems measures the accumulated time of such actions in each frame. Such actions may be: shader compilation, present, memory mapping, and more. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.īrain–computer interface convolutional neural network deep learning deep neural networks functional near-infrared spectroscopy long short-term memory mental workload.Ĭopyright © 2020 Asgher, Khalil, Khan, Ahmad, Butt, Ayaz, Naseer and Nazir.Ībibullaev B., Jinung A. This is a great tool for detecting the reason for frame time stuttering. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. 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. 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. The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). (my program is using openmp multithreads) I’m seeing that cudaLaunch takes 280 ns (min) and 58ms (max). The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. I’m profiling my CUDA program, using nvprof. Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics.
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