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MWL assessment is important to increase the safety and efficiency in brain\u2013computer 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 cascaded1DCNN-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 sevenfold cross-validation, respectively.<\/jats:p>","DOI":"10.1007\/s44230-024-00086-y","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T17:13:59Z","timestamp":1731431639000},"page":"599-609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An End-to-End Brain Computer Interface System for Mental Workload Estimation through Hybrid Deep Learning Model"],"prefix":"10.1007","volume":"4","author":[{"given":"Vipul","family":"Sharma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5346-7008","authenticated-orcid":false,"given":"Mitul Kumar","family":"Ahirwal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"issue":"20","key":"86_CR1","doi-asserted-by":"publisher","first-page":"24135","DOI":"10.1109\/JSEN.2023.3312172","volume":"23","author":"A Othmani","year":"2023","unstructured":"Othmani A, Brahem B, Haddou Y, Mustaqeem. 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