A5000766901- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
- Gaze Tracking and Assistive Technology
- Functional Brain Connectivity Studies
- Neural and Behavioral Psychology Studies
- Ultrasonics and Acoustic Wave Propagation
- Non-Destructive Testing Techniques
- Visual perception and processing mechanisms
- Advanced Computational Techniques and Applications
- Transcranial Magnetic Stimulation Studies
- Geological Modeling and Analysis
- Neural Networks and Applications
- Multisensory perception and integration
- ECG Monitoring and Analysis
- Simulation and Modeling Applications
- Railway Engineering and Dynamics
- Non-Invasive Vital Sign Monitoring
- Dementia and Cognitive Impairment Research
- Structural Health Monitoring Techniques
- Air Quality Monitoring and Forecasting
- Sleep and Work-Related Fatigue
- Photoreceptor and optogenetics research
Tsinghua University
2016-2025
State Key Laboratory on Integrated Optoelectronics
2020-2024
Chinese Academy of Sciences
2019-2024
Institute of Semiconductors
2020-2024
Shanghai International Studies University
2024
Southwest Jiaotong University
2009-2024
Lanzhou Jiaotong University
2022-2024
Ministry of Natural Resources
2023-2024
Sinopec (China)
2006-2024
Liaoning Normal University
2024
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence this method extract a narrowband component SSVEP EEG. A recognition approach proposed based on extracted features for an SSVEP-based brain computer interface (BCI). Recognition Results were higher than those using widely used FFT (fast Fourier transform)-based spectrum estimation
Significance Brain–computer interface (BCI) technology provides a new communication channel. However, current applications have been severely limited by low speed. This study reports noninvasive brain speller that achieved multifold increase in information transfer rate compared with other existing systems. Based on extremely precise coding of frequency and phase single-trial steady-state visual evoked potentials, this developed joint frequency-phase modulation method user-specific decoding...
In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically relation to speed, user variation and ease use. With these improvements mind, this paper presents an online multi-channel system a canonical correlation analysis (CCA) method for extraction frequency information associated with the SSVEP. The key parameters,...
This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward high-speed brain-computer interface (BCI) speller.
Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which make use of harmonic SSVEP components enhance the CCA-based frequency detection not well established. Approach. This study proposed filter bank (FBCCA) incorporate fundamental improve SSVEPs. A 40-target BCI speller based on coding...
With the development of brain-computer interface (BCI) technology, researchers are now attempting to put current BCI techniques into practical application. This paper presents an environmental controller using a technique based on steady-state visual evoked potential. The system is composed stimulator, digital signal processor, and trainable infrared remote-controller. attractive features this include noninvasive recording, little training requirement, high information transfer rate. Our...
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence this method extract a narrowband component SSVEP EEG. A recognition approach proposed based on extracted features for an SSVEP-based brain computer interface (BCI). Recognition Results were higher than those using widely used fast Fourier transform (FFT)-based spectrum estimation
This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with 40-target brain- computer interface (BCI) speller. The consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed cue-guided target selecting task. virtual keyboard the speller was composed 40 flickers, which were coded using joint frequency phase modulation (JFPM) approach. stimulation frequencies ranged 8 Hz to 15.8 an...
A brain-computer interface(BCI) based on motor imagery (MI) translates the subject's intention into a control signal through classifying electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for pattern identification therefore MI-based BCI is still in stage laboratory demonstration, to some extent, due need constanly troublesome recording...
Recently, electroencephalogram-based brain–computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, low communication speed current BCI systems greatly limits practical application. In this paper, we present a high-speed based on code modulation visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by time-shifted binary pseudorandom sequence. A multichannel identification...
Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR speller based on the steady-state visual evoked potential (SSVEP). A 45-target was implemented with frequency resolution 0.2 Hz. sampled sinusoidal stimulation method used present stimuli conventional LCD screen. The online results revealed that proposed had good...
Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, named Conformer, encapsulate global in unified classification framework. Specifically, convolution module learns low-level throughout one-dimensional spatial layers. The self-attention is straightforwardly connected correlation within features. Subsequently,...
The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement been made frequency recognition method data sharing. However, number of pubic databases is still limited this field. Therefore, we present a BEnchmark database Towards BCI Application (BETA) study. BETA composed 64-channel Electroencephalogram (EEG) 70 subjects...
A brain-computer interface (BCI) provides a direct communication channel between brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) recent state-of-the-art method individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may...
Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks even irreversible damage to soft tissues. Here, we propose in-ear visual auditory BCIs based on bioelectronics, named as SpiralE, can adaptively expand spiral along the meatus under...
A brain computer interface (BCI) translates human intentions into control signals to establish a direct communication channel between the and external devices. Because BCI does not depend on brain's normal output pathways of peripheral nerves muscles, it can provide new people with severe motor disabilities. Electroencephalograms (EEGs) recorded from surface scalp are widely used in current BCIs for their non-invasive nature easy applications. Among EEG based BCIs, systems visual evoked...
In most current motor-imagery-based brain-computer interfaces (BCIs), machine learning is carried out in two consecutive stages: feature extraction and classification. Feature has focused on automatic of spatial filters, with little or no attention being paid to optimization parameters for temporal filters that still require time-consuming, ad hoc manual tuning. this paper, we present a new algorithm termed iterative spatio-spectral patterns (ISSPL) employs statistical theory perform...
Common spatial patterns (CSP) is a well-known filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP in probabilistic modeling setting. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> CSP</i> (P-CSP) proposed as generic EEG spatio-temporal framework that subsumes and regularized algorithms. The enables us to resolve overfitting issue of principled manner. We derive statistical...
This paper presents a novel brain-computer interface (BCI) based on motion-onset visual evoked potentials (mVEPs). mVEP has never been used in BCI research, but widely studied basic research. For the application, brief motion of objects embedded into onscreen virtual buttons is to evoke that time locked onset motion. EEG data registered from 15 subjects are investigate spatio-temporal pattern this paradigm. N2 and P2 components, with distinct temporo-occipital parietal topography,...
Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even most commonly used metric, information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which not conducive BCI research. This paper aims point out of existing problems and give some suggestions methods overcome these problems.First, preconditions inherent ITR calculation based on Wolpaw's definition are summarized several incorrect...
Although robot technology has been successfully used to empower people who suffer from motor disabilities increase their interaction with physical environment, it remains a challenge for individuals severe impairment, do not have the control ability move robots or prosthetic devices by manual control. In this study, mitigate issue, noninvasive brain-computer interface (BCI)-based robotic arm system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented...
In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA employed to improve the performance SSVEP-based brain-computer interface (BCI) system standard CCA. SSVEP response phases are estimated based on physiologically meaningful apparent latency and added as reliable constraint into The results EEG experiments involving 10...
Objective. A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) using joint frequency-phase coding. Approach. The key idea is to transfer SSVEP templates from the existing subjects a subject enhance of SSVEPs. Under this framework, template-based canonical correlation analysis (tt-CCA) methods were developed single-channel and multi-channel conditions respectively. In addition, an online CCA...