Label-efficient time series representation learning: A review
The scarcity of labeled data is one of the main challenges of applying deep learning models
on time series data in the real world. Therefore, several approaches, eg, transfer learning …
on time series data in the real world. Therefore, several approaches, eg, transfer learning …
Human lower limb motion intention recognition for exoskeletons: A review
LL Li, GZ Cao, HJ Liang, YP Zhang… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Human motion intention (HMI) has increasingly gained concerns in lower limb exoskeletons
(LLEs). HMI recognition (HMIR) is the precondition for realizing active compliance control in …
(LLEs). HMI recognition (HMIR) is the precondition for realizing active compliance control in …
[HTML][HTML] Improving multi-class motor imagery EEG classification using overlapping sliding window and deep learning model
Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems.
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis
Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
Multiscale convolutional transformer for EEG classification of mental imagery in different modalities
A new kind of sequence–to–sequence model called a transformer has been applied to
electroencephalogram (EEG) systems. However, the majority of EEG–based transformer …
electroencephalogram (EEG) systems. However, the majority of EEG–based transformer …
ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network
Y Qin, B Li, W Wang, X Shi, H Wang, X Wang - Brain Research, 2024 - Elsevier
Brain-computer interface (BCI) enables the control of external devices using signals from the
brain, offering immense potential in assisting individuals with neuromuscular disabilities …
brain, offering immense potential in assisting individuals with neuromuscular disabilities …
[HTML][HTML] Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees
The analysis of Electroencephalography (EEG) signals has been an effective way of eye
state identification. Its significance is highlighted by studies that examined the classification …
state identification. Its significance is highlighted by studies that examined the classification …
[PDF][PDF] 基于粒子群优化支持向量机康复下肢外骨骼的脑电控制研究
毕文龙， 魏笑， 谭草， 赵彦峻， 刘文龙 - 科学技术与工程, 2023 - stae.com.cn
基于粒子群优化支持向量机康复下肢外骨骼的脑电控制研究 Page 1 投稿网址:www. stae. com.
cn 2023 年第23 卷第16 期 2023, 23(16): 06952 -07 科学技术与工程 Science Technology and …
cn 2023 年第23 卷第16 期 2023, 23(16): 06952 -07 科学技术与工程 Science Technology and …
Incorporating hand-crafted features into deep learning models for motor imagery EEG-based classification
P Bustios, JL Garcia Rosa - Applied Intelligence, 2023 - Springer
Motor imagery (MI) is a mental process that produces two types of event-related potentials
called event-related desynchronization (ERD) and event-related synchronization (ERS). We …
called event-related desynchronization (ERD) and event-related synchronization (ERS). We …
[HTML][HTML] Functional mapping of the brain for brain–computer interfacing: A review
Brain–computer interfacing has been applied in a range of domains including rehabilitation,
neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the …
neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the …