An Improved Adaptive Monte Carlo Localization Algorithm Integrated with a Virtual Motion Model.

Regarding the issue of high dependency on odometry in the adaptive Monte Carlo localization (AMCL) algorithm, an improved AMCL algorithm based on the normal distributions transform (NDT) and extended Kalman filter (EKF) is proposed. A virtual motion model is introduced into the AMCL framework to enable pose updates even when the robot has not moved. NDT is used for point cloud matching to estimate virtual displacement and calculate virtual control quantities, which are then fed into the motion model to predict and update particle states when the robot has not moved.

Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain-computer interface.

Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain-computer interfaces (BCIs) in this study.

[Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character patterns].

The traditional paradigm of motor-imagery-based brain-computer interface (BCI) is abstract, which cannot effectively guide users to modulate brain activity, thus limiting the activation degree of the sensorimotor cortex. It was found that the motor imagery task of Chinese characters writing was better accepted by users and helped guide them to modulate their sensorimotor rhythms. However, different Chinese characters have different writing complexity (number of strokes), and the effect of motor imagery tasks of Chinese characters with different writing complexity on the performance of motor-imagery-based BCI is still unclear.

Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window.

Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.

Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems.

Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces.

. Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources.

Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces.

Background: Motor imagery (MI) related features are typically extracted from a fixed frequency band and time window of EEG signal. Meanwhile, the time when the brain activity associated with the occurring task varies from person to person and trial to trial. Thus, some of the discarded EEG data with time may contain MI-related information.

Novel hybrid brain-computer interface system based on motor imagery and P300.

Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training.

Effects of Visual Attention on Tactile P300 BCI.

. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed.

Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns.

Objective: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns.

Correlation-based channel selection and regularized feature optimization for MI-based BCI.

Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks.