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IEEE Transactions on Cognitive and Developmental Systems is a peer-reviewed journal published by IEEE, covering EEG and Brain-Computer Interfaces, Reinforcement Learning in Robotics, Robot Manipulation and Learning. Over its lifetime it has published 1,383 papers, which have received 22,536 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Cognitive and Developmental Systems style. No sign-up. |
| Abbreviation (ISO-4) | TCDS |
| Publishes research in | EEG and Brain-Computer Interfaces Reinforcement Learning in Robotics Robot Manipulation and Learning Neural dynamics and brain function Ferroelectric and Negative Capacitance Devices |
| ISSN | 2379-8920 |
| Citation impact (2-yr) | 3.32 |
| h-index | 66 |
| i10-index | 548 |
| Top institutions publishing here | Chinese Academy of Sciences |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively. In this article, we compare the recognition performance and robustness of two multimodal emotion recognition models: 1) deep canonical correlation analysis (DCCA) and 2) bimodal deep autoencoder (BDAE). The contributions of this article are threefold: 1) we propose two methods for extending…
A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during…
Affective brain-computer interface (aBCI) introduces personal affective factors to human-computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to achieve accurate emotion classification. A subject-independent classifier that is trained on pooled data from multiple subjects generally leads to inferior accuracy, due to the fact that electroencephalography patterns vary from subject to subject.…
Neuroscience study has revealed the discrepancy of emotion expression between the left and right hemispheres of human brain. Inspired by this study, in this article, we propose a novel bi-hemispheric discrepancy model (BiHDM) to learn this discrepancy information between the two hemispheres to improve electroencephalograph (EEG) emotion recognition. Concretely, we first employ four directed recurrent…
In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional…
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