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Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness
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In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.sorbonne-universite.fr/hal-03640595 ; Frontiers in Neuroscience, Frontiers, 2022, 16, pp.801666. ⟨10.3389/fnins.2022.801666⟩ (2022)
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| 2 |
Still I Aspire: Graduate Degree Aspirations for Community College Transfer Students of Color
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| 3 |
MAGIC DUST FOR CROSS-LINGUAL ADAPTATION OF MONOLINGUAL WAV2VEC-2.0
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In: ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03544515 ; ICASSP 2022, May 2022, Singapour, Singapore (2022)
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| 4 |
Cross-lingual few-shot hate speech and offensive language detection using meta learning
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559484 ; IEEE Access, IEEE, 2022, 10, pp.14880-14896. ⟨10.1109/ACCESS.2022.3147588⟩ (2022)
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| 5 |
Machine-readable Finnish-Livvi bilingual translation dictionary ...
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| 6 |
Machine-readable Finnish-Karelian bilingual translation dictionary ...
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| 7 |
Machine-readable Finnish-Karelian bilingual translation dictionary ...
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| 8 |
Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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| 9 |
Machine-readable Finnish-Livvi bilingual translation dictionary ...
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| 10 |
Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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| 11 |
TEACHING ENGLISH FOR THE SECOND LANGUAGE STUDENTS AS THE SECOND LANGUAGE ...
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| 12 |
TEACHING ENGLISH FOR THE SECOND LANGUAGE STUDENTS AS THE SECOND LANGUAGE ...
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| 14 |
ECONOMIC TERMS IN THE LEXICAL SYSTEM OF THE MODERN UZBEK LANGUAGE ...
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| 16 |
ECONOMIC TERMS IN THE LEXICAL SYSTEM OF THE MODERN UZBEK LANGUAGE ...
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| 17 |
Visual generics: How children understand generic language with different visualizations ...
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| 18 |
Attributions of Successful English Language Learners in Transfer-Level English
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In: Doctoral Dissertations and Projects (2022)
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| 19 |
Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity
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In: Journal of Imaging; Volume 8; Issue 4; Pages: 86 (2022)
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| 20 |
Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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Abstract:
Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.
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Keyword:
classification; electroencephalography (EEG); mental workload; online BCI; passive brain–computer interface; stress; transfer learning
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URL: https://doi.org/10.3390/s22020535
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