DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets
<italic>Objective:</italic> Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficien...
Saved in:
| Main Authors: | Liangwei Fan, Hui Shen, Fengyu Xie, Jianpo Su, Yang Yu, Dewen Hu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9801685/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An EEG dataset for studying asynchronous steady-state visual evoked potential (SSVEP) based brain computer interfaces
by: Jing Zhao, et al.
Published: (2024-12-01) -
Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation
by: Byung-Kwan Ko, et al.
Published: (2025-01-01) -
EEG-based Incongruency Decoding in AR with sLDA, SVM, and EEGNet
by: Wimmer Michael, et al.
Published: (2024-10-01) -
Real-Time EEG-Based BCI for Self-Paced Motor Imagery and Motor Execution Using Functional Neural Networks
by: Mavin Heim, et al.
Published: (2025-01-01) -
Performance Enhancement of an SSVEP-Based Brain–Computer Interface in Augmented Reality Through Adaptive Color Adjustment of Visual Stimuli for Optimal Background Contrast
by: Cheong-Un Kim, et al.
Published: (2025-01-01)