A high-speed hybrid brain-computer interface with more than 200 targets
Objective. Brain-computer interfaces (BCIs) have recently made significant strides in
expanding their instruction set, which has attracted wide attention from researchers. The …
expanding their instruction set, which has attracted wide attention from researchers. The …
Upregulation of p300 in paclitaxel-resistant TNBC: implications for cell proliferation via the PCK1/AMPK axis
PW Zhao, JX Cui, XM Wang - The Pharmacogenomics Journal, 2024 - nature.com
Objective To explore the role of p300 in the context of paclitaxel (PTX) resistance in triple-
negative breast cancer (TNBC) cells, focusing on its interaction with the …
negative breast cancer (TNBC) cells, focusing on its interaction with the …
A novel command generation method for SSVEP-based BCI by introducing SSVEP blocking response
X Yuan, L Zhang, Q Sun, X Lin, C Li - Computers in Biology and Medicine, 2022 - Elsevier
Increasing the number of commands in a steady-state visual evoked potential (SSVEP)-
based brain-computer interface (BCI) by increasing the number of visual stimuli has been …
based brain-computer interface (BCI) by increasing the number of visual stimuli has been …
Inter-participant Transfer Learning with Attention based Domain Adversarial Training for P300 Detection
A Brain-computer interface (BCI) system establishes a novel communication channel
between the human brain and a computer. Most event related potential-based BCI …
between the human brain and a computer. Most event related potential-based BCI …
MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response
to external events and their associated underlying complex spatiotemporal feature …
to external events and their associated underlying complex spatiotemporal feature …
[HTML][HTML] Hybrid SSVEP+ P300 brain-computer interface can deal with non-stationary cerebral responses with the use of adaptive classification
DD Kapgate - Journal of Neurorestoratology, 2024 - Elsevier
Introduction The non-stationarity of electroencephalograms (EEG) has a substantial effect on
the performance of classifiers in brain-computer interface (BCI) systems. To tackle this …
the performance of classifiers in brain-computer interface (BCI) systems. To tackle this …
[HTML][HTML] A Novel Asynchronous Brain Signals-Based Driver–Vehicle Interface for Brain-Controlled Vehicles
Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may
help people with neuromuscular disorders regain their driving ability. In this paper, we …
help people with neuromuscular disorders regain their driving ability. In this paper, we …
Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset
R Khan, C Xiao, Y Liu, J Tian, Z Chen, L Su… - Interdisciplinary …, 2024 - Springer
Kidney ultrasound (US) images are primarily employed for diagnosing different renal
diseases. Among them, one is renal localization and detection, which can be carried out by …
diseases. Among them, one is renal localization and detection, which can be carried out by …
QTFD 与DenseNet 相结合的运动想象分类方法.
金晶， 杨益雕， 孙浩， 王行愚 - Journal of Signal Processing, 2023 - search.ebscohost.com
运动想象脑机接口(Motor Imagery Brain Computer Interface, MI-BCI) 技术近年来在医疗康复,
娱乐等许多领域得到了广泛的运用. 然而, 如何处理非平稳的脑电信号(Electroencephalography …
娱乐等许多领域得到了广泛的运用. 然而, 如何处理非平稳的脑电信号(Electroencephalography …
Adaptive classification helps hybrid visual brain computer interface systems handle non‐stationary cortical signals
DD Kapgate, K Prasad. K - Cognitive Computation and …, 2023 - Wiley Online Library
The classifier efficiency of the brain‐computer interface systems is significantly impacted by
the non‐stationarity of electroencephalogram (EEG) signals. We propose an adaptive …
the non‐stationarity of electroencephalogram (EEG) signals. We propose an adaptive …