An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue.
Sign Up to like & getrecommendations! Published in 2019 at "Journal of neural engineering"
DOI: 10.1088/1741-2552/ab255d
Abstract: Objective. This paper proposes an iterative negative-unlabeled (NU) learning algorithm for cross-subject detection of passive fatigue from labelled alert (negative) and unlabeled driving EEG data. Approach. Unlike other studies which used manual labeling of the… read more here.
Keywords: cross subject; negative unlabeled; fatigue; passive fatigue ... See more keywords
Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac0489
Abstract: Objective. Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical… read more here.
Keywords: cross subject; selection; channel selection; subject generalization ... See more keywords
Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac7d73
Abstract: Objective. Multi-channel electroencephalogram data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery (MI)-based brain-computer interface (BCI) systems. Therefore, channel selection can… read more here.
Keywords: cross subject; subject generalization; channel selection; multi ... See more keywords
Improving Cross-Subject Activity Recognition via Adversarial Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2993818
Abstract: Deep learning has been widely used for implementing human activity recognition from wearable sensors like inertial measurement units. The performance of deep activity recognition is heavily affected by the amount and variability of the labeled… read more here.
Keywords: performance; activity; cross subject; activity recognition ... See more keywords
Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3204739
Abstract: Human activities recognition (HAR) plays a vital role in fields like ambient assisted living and health monitoring, in which cross-subject recognition is one of the main challenges coming from the diversity of various users. Although… read more here.
Keywords: cross subject; activities recognition; cross; human activities ... See more keywords
MASS: A Multisource Domain Adaptation Network for Cross-Subject Touch Gesture Recognition
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3174063
Abstract: Touch gesture recognition (TGR) plays a pivotal role in many applications, such as socially assistive robots and embodied telecommunication. However, one obstacle to practicality of existing TGR methods is the individual disparities across subjects. Moreover,… read more here.
Keywords: cross subject; network; tgr; domain ... See more keywords
A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3216528
Abstract: Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper,… read more here.
Keywords: cross subject; kinematics surface; kinematics; hand kinematics ... See more keywords
SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.958052
Abstract: The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification… read more here.
Keywords: cross subject; eeg classification; symmetric positive; classification ... See more keywords
Improving the Cross-Subject Performance of the ERP-Based Brain–Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Human Neuroscience"
DOI: 10.3389/fnhum.2020.00296
Abstract: The brain–computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range… read more here.
Keywords: erp; cross subject; bci; computer ... See more keywords
Multi-Class Transfer Learning and Domain Selection for Cross-Subject EEG Classification
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13085205
Abstract: Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and… read more here.
Keywords: cross subject; classification; class; transfer learning ... See more keywords
Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24050705
Abstract: With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with… read more here.
Keywords: cross subject; emotion recognition; subject emotion; multi classifier ... See more keywords