Related papers: Zero-calibration cVEP BCI using word prediction: a…
The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs)…
A code-modulated motion visual evoked potential (c-MVEP) for brain-computer interfacing (BCI) is presented in this study. This paradigm uses pseudo-random sequences to visually stimulate objects using motion as an alternative to flickering.…
A limitation of brain-computer interface (BCI) spellers is that they require the user to be able to move the eyes to fixate on targets. This poses an issue for users who cannot voluntarily control their eye movements, for instance, people…
We present results of an approach to a code-modulated visual evoked potential (cVEP) based brain-computer interface (BCI) paradigm using four high-frequency flashing stimuli. To generate higher frequency stimulation compared to the…
Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices…
This study explores two zero-training methods aimed at enhancing the usability of brain-computer interfaces (BCIs) by eliminating the need for a calibration session. We introduce a novel method rooted in the event-related potential (ERP)…
Speeding up the spelling in event-related potentials (ERP) based Brain-Computer Interfaces (BCI) requires eliciting strong brain responses in a short span of time, as much as the accurate classification of such evoked potentials remains…
Brain-Computer Interfaces (BCIs) implement a direct communication pathway between the brain of an user and an external device, as a computer or a machine in general. One of the most used brain responses to implement non-invasive BCIs is the…
Foundation Models (FMs) have surged in popularity over the past five years, with applications spanning fields from computer vision to natural language processing. Brain-Computer Interfaces (BCIs) have also gained momentum due to their…
Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech from neural activity. However, decoding…
An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to…
Steady-State Visual Evoked Potential is a brain response to visual stimuli flickering at constant frequencies. It is commonly used in brain-computer interfaces for direct brain-device communication due to their simplicity, minimal training…
The purpose of this study is to develop a new methodology for designing stimulus sequences for cVEP BCI based on experimental studies regarding the behavior and the properties of the actual EEG responses of the visual system to coded visual…
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms…
A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example,…
Steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI) provides reliable responses leading to high accuracy and information throughput. But achieving high accuracy typically requires a relatively long time window of…
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data…
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting Event-Related Potentials (ERPs). The current RSVP decoding methods can perform well in…
Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and session-dependent noise in EEG signals. This study proposes…
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram…