Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (st...
As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. H...
The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information tr...
The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain–computer interface (BCI) spellers. In BCI spellers,...
Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric...
In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain–computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus par...
Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little...
Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target fre...
15 September 2016
Achieving attention tracking as easily as recording eye movements is still beyond reach. However, by exploiting Steady-State Visual Evoked Potentials (SSVEPs) we could recently record in a satisfactory way the horizontal trajectory of covert visuospa...
As a widely used brain–computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse user...
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying el...
Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) have been well researched due to their easy system configuration, little or no user training and high information transfer rates. To elicit an SSVEP, a r...
Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-bas...
This paper addresses steady-state visually evoked potential (SSVEP) classification in wearable extended-reality (XR) brain–computer interfaces (BCIs), with a threefold objective. First, it investigates the effectiveness of deep learning (DL)-ba...
Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are...
In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) speller application. According to results from our...
The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG chann...
As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources a...
Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implement...
Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver’s attention, potentially causing critical car accidents. In the present study,...
The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SS...
A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interact...
The individual-specific steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) is characterized by individual calibration data, resulting in satisfactory performance. However, existing individual-specific SSVEP-BCIs e...
Maintaining optimal levels of engagement during rehabilitation training is crucial for inducing neuroplasticity in the motor cortex, which directly influences positive rehabilitation outcomes. In this research article, we propose a virtual reality (V...
Steady-State Visual Evoked Potentials (SSVEPs) offer a robust basis for brain–computer interface (BCI) systems due to their high signal-to-noise ratio, minimal user training requirements, and suitability for real-time decoding. In this work, we...
Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively l...
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) m...
Background: Brain–Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by...
The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI syst...
Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific informa...
The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educa...
This article presents a framework for brain–computer interfaces (BCIs) based on the motor imagery (MI) and steady-state visually evoked potential (SSVEP) paradigms, with a focus on rehabilitation and robotics applications. The development of th...
Background/Objectives: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain–computer interface (BCI) methods. The goal is to assess the possib...
A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily...
Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to...
Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EE...
Brain–computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the si...
Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a n...
Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapi...
Color vision deficiency (CVD) is one of the most common disorders related to visual impairment. Individuals with this condition are unable to differentiate between colors due to the absence or impairment of one or more color photoreceptors in their r...
Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain–computer interfacing (BCI) is assessed. The novel fe...
Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED stimulator system explicitly designed for steady-state visual stimulation, o...
This study investigated performance characteristics and channel selection strategies for single-flicker steady-state visual evoked potential (SSVEP) brain–computer interfaces (BCIs) using minimal recording channels. SSVEP clustering patterns fr...
Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain&ndash...
Visuo-spatial attention acts as a filter for the flood of visual information. Until recently, experimental research in this area focused on neural dynamics of shifting attention in 2D space, leaving attentional shifts in depth less explored. In this...
This work presents a study of chromatic and luminance stimuli in low-, medium-, and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in the behind-the-ears area. Twelve healthy subjects participated in this study. The...
Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application...
In this paper, a bibliometric review is conducted on brain–computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An explo...
The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli use...
Brain–Computer Interfaces (BCI) using Steady-State Visual Evoked Potentials (SSVEP) are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA) is seen as state-of-the-art for SSVEP BCI systems. However, thi...
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