Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Tianheng Xu"'
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
ObjectiveThe brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroe
Externí odkaz:
https://doaj.org/article/cc5d9c1b46ce4818b72f1f98aa98c246
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 1687-1702 (2024)
As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power
Externí odkaz:
https://doaj.org/article/28851dbf93744ecd8579654ad3d3fa53
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising can
Externí odkaz:
https://doaj.org/article/0da69a2bfa5040f7a9860309ede8fc28
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4760-4772 (2023)
Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to en
Externí odkaz:
https://doaj.org/article/e0f29adac676401b9b0e96f596943917
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4402-4412 (2023)
As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by ext
Externí odkaz:
https://doaj.org/article/cf326abccb854d91beb27204730e39f9
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 1743-1753 (2023)
In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of s
Externí odkaz:
https://doaj.org/article/5ecfb0db09524d319319e8ad36b28e22
Publikováno v:
Applied Sciences, Vol 13, Iss 21, p 11924 (2023)
Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recogni
Externí odkaz:
https://doaj.org/article/e80a4fe7b34b4074adbb17a5f5348872
Publikováno v:
Entropy, Vol 25, Iss 9, p 1304 (2023)
Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in
Externí odkaz:
https://doaj.org/article/a62dbff83ea64ff08289860ad82120b9
Publikováno v:
IEEE Access, Vol 10, Pp 73257-73268 (2022)
Brain-computer interface based on steady-state visual evoked potential (SSVEP-BCI) has been widely concerned because of its highest SNR of EEG signals. This kind of BCIs needs to modulate the control instructions with visual stimuli. However, a large
Externí odkaz:
https://doaj.org/article/42d3b74a036d4e9f92a2b545702ded3a
Publikováno v:
Sensors, Vol 23, Iss 10, p 4721 (2023)
For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial–temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts ha
Externí odkaz:
https://doaj.org/article/5ddf172b6056459b845d454e42159517