An attention-aware long short-term memory-like spiking neural model for sentiment analysis
Q Liu, Y Huang, Q Yang, H Peng… - International journal of …, 2023 - World Scientific
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
Perceptual metric-guided human image generation
H Wu, F He, Y Duan, X Yan - Integrated Computer-Aided …, 2022 - content.iospress.com
Pose transfer, which synthesizes a new image of a target person in a novel pose, is valuable
in several applications. Generative adversarial networks (GAN) based pose transfer is a new …
in several applications. Generative adversarial networks (GAN) based pose transfer is a new …
A method based on evolutionary algorithms and channel attention mechanism to enhance cycle generative adversarial network performance for image translation
A Generative Adversarial Network (GAN) can learn the relationship between two image
domains and achieve unpaired image-to-image translation. One of the breakthroughs was …
domains and achieve unpaired image-to-image translation. One of the breakthroughs was …
Feature selection combining filter and wrapper methods for motor-imagery based brain–computer interfaces
Motor imagery (MI) based brain–computer interfaces help patients with movement disorders
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
Optimization of model training based on iterative minimum covariance determinant in motor-imagery BCI
The common spatial patterns (CSP) algorithm is one of the most frequently used and
effective spatial filtering methods for extracting relevant features for use in motor imagery …
effective spatial filtering methods for extracting relevant features for use in motor imagery …
Feature selection method based on Menger curvature and LDA theory for a P300 brain–computer interface
Objective. Brain–computer interface (BCI) systems decode electroencephalogram (EEG)
signals to establish a channel for direct interaction between the human brain and the …
signals to establish a channel for direct interaction between the human brain and the …
Detection of movement intention in eeg-based brain-computer interfaces using fourier-based synchrosqueezing transform
N Karakullukcu, B Yilmaz - International journal of neural systems, 2022 - World Scientific
Patients with motor impairments need caregivers' help to initiate the operation of brain-
computer interfaces (BCI). This study aims to identify and characterize movement intention …
computer interfaces (BCI). This study aims to identify and characterize movement intention …
Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain–computer interface
Objective. Spatial and spectral features extracted from electroencephalogram (EEG) are
critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the …
critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the …
The influence of vibro-tactile finger stimulation parameters on P300 characteristics changes for rehabilitation: pilot study on healthy subjects
I Gremitskiy, I Levadniy… - 2021 Ural Symposium on …, 2021 - ieeexplore.ieee.org
Up-to-date brain-computer interfaces based on the P300 neuromechanics most commonly
apply visual and auditory stimulation. However, P300 based on vibro-tactile stimulation has …
apply visual and auditory stimulation. However, P300 based on vibro-tactile stimulation has …
Human-Robot Interaction Based on Biosignals
This paper introduces a novel manner for human-robot interaction (HRI) based on
electroencephalogram (EEG) and surface electromyography (sEMG) signals. The P300 …
electroencephalogram (EEG) and surface electromyography (sEMG) signals. The P300 …