Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces.

Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%-30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates.

Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.

Keyboards and smartphones allow users to express their thoughts freely via manual control. Hands-free communication can be realized with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). Various variations of such spellers have been developed: Low-target systems, multi-target systems and systems with dictionary support.

Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs.

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 previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects.

Five Shades of Grey: Exploring Quintary -Sequences for More User-Friendly c-VEP-Based BCIs.

Responsive EEG-based communication systems have been implemented with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). The BCI targets are typically encoded with binary -sequences because of their autocorrelation property; the digits one and zero correspond to different target colours (usually black and white), which are updated every frame according to the code. While binary flickering patterns enable high communication speeds, they are perceived as annoying by many users.

Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes.

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 situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately.

Different Feedback Methods For An SSVEP-Based BCI.

In this paper we examined different ways to inform the user of the classification progress in our online SSVEPbased BCI speller. Different user feedback was given based on the distance from the classification threshold, separately calculated for each stimulus. We focused on the comparison of the accuracies and spelling times associated with each different feedback type.

Brain-Computer Interface Spellers: A Review.

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field.