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   Articles with "cross subject" as a keyword



An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue.

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recommendations! 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

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recommendations! 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

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recommendations! 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

An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices

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recommendations! Published in 2024 at "Journal of Neural Engineering"

DOI: 10.1088/1741-2552/ad3eb3

Abstract: Objective.This paper presents data-driven solutions to address two challenges in the problem of linking neural data and behavior: (1) unsupervised analysis of behavioral data and automatic label generation from behavioral observations, and (2) extraction of… read more here.

Keywords: autoencoder; variational autoencoder; neural decoding; adversarial variational ... See more keywords

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning

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recommendations! Published in 2024 at "Journal of Neural Engineering"

DOI: 10.1088/1741-2552/ad618a

Abstract: Objective. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in… read more here.

Keywords: detection; framework; pairwise learning; cross subject ... See more keywords

Improving Cross-Subject Activity Recognition via Adversarial Learning

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recommendations! 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

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recommendations! 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

Evolutionary Ensemble Learning for EEG-Based Cross-Subject Emotion Recognition

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recommendations! Published in 2024 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2024.3384816

Abstract: Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose… read more here.

Keywords: cross subject; emotion recognition; emotion; subject emotion ... See more keywords

Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification

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recommendations! Published in 2024 at "IEEE Journal of Biomedical and Health Informatics"

DOI: 10.1109/jbhi.2024.3454158

Abstract: Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model… read more here.

Keywords: classification; ssvep; cross subject; transformer ... See more keywords

Prediction Consistency and Confidence-Based Proxy Domain Construction for Privacy-Preserving in Cross-Subject EEG Classification.

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recommendations! Published in 2025 at "IEEE journal of biomedical and health informatics"

DOI: 10.1109/jbhi.2025.3595826

Abstract: Domain adaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects while only unlabeled data is provided for target subjects. Existing… read more here.

Keywords: proxy domain; subject eeg; source domain; source ... See more keywords

Dual-Branch Attention-based Frequency Domain Network for Cross-subject SSVEP-BCIs.

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recommendations! Published in 2025 at "IEEE journal of biomedical and health informatics"

DOI: 10.1109/jbhi.2025.3630249

Abstract: Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) hold significant promise for enabling high-speed human-computer interaction in real-world scenarios. However, existing frequency-domain decoding methods treat frequency spectrum features (the real and imaginary spectrum features) as a… read more here.

Keywords: frequency domain; cross subject; branch; branch attention ... See more keywords