Cognitive Computing for Brain–Computer Interface-Based Computational Social Digital Twins Systems
Bibliographic Data
| ID | 22106931 |
|---|---|
| Authors | Zhihan Lv (0000-0003-2525-3074, Uppsala University), Liang Qiao (0000-0002-9731-4166, Qingdao University), Haibin Lv (0000-0003-1059-4765, National Bureau of Statistics of China) |
| Year | 2022 |
| Volume | 9 |
| Issue | 6 |
| Pages | 1635-1643 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3202872 |
| OpenAlex | W4295886291 |
| Language | EN |
| Citations received | 5 |
| References cited | 35 |
To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain–computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system
Algorithm · Brain–computer interface · Computer vision · Electroencephalography · Feature extraction · Advanced Memory and Neural Computing · Advanced Technologies and Applied Computing · Computer Science · EEG and Brain-Computer Interfaces · Artificial Intelligence
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A Novel Intelligence Evaluation Framework
Discriminative Adversarial Network Based on Spatial–Temporal–Graph Fusion for Motor Imagery Recognition
A Spectral-Temporal Refined Attention Network via Contrastive Mutual Learning for Closed-Loop Motor Imagery BCI
Sociolinguistic Radar of Phonological Variation and Social Meaning
| Unique citing works | 5 |
|---|---|
| Citations per year | 2,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |