An End-to-End Brain Computer Interface System for Mental Workload Estimation through Hybrid Deep Learning Model

An End-to-End Brain Computer Interface System for Mental Workload Estimation through Hybrid Deep Learning Model

Abstract In this paper, a new cascade one-dimensional convolutional 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 br...

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Bibliographic Details
Main Authors: Vipul Sharma, Mitul Kumar Ahirwal
Format: Article
Language:English
Published: Springer Nature 2024-11-01
Series:Human-Centric Intelligent Systems
Subjects:
Bidirectional long short-term memory
Brain–computer interface
Multivariate time series classification
Convolutional neural networks
Deep learning
Electroencephalography
Online Access:https://doi.org/10.1007/s44230-024-00086-y
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