A brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroenc...

P300 speller is a system that allows users to input letters using only electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to achieve high performance in P300 speller, achieving high performance of P300 detection is essential. However, EEG waveforms are strongly dependent on the conditions of subject and/or environment, so it is not easy to...

The P300 speller allows users to select letters just by thoughts. However, due to the low signal-to-noise ratio of the P300 response, signal averaging is often performed, which improves the spelling accuracy but degrades the spelling speed. The authors have proposed reliability-based automatic repeat request (RB-ARQ) to ease this problem. RB-ARQ could be enhanced when it is combined with the er...

Brain Computer Interfaces (BCI) provide a non-muscular communication channel for people with severe motor impairment. The most commonly used BCI for communication is the so called P300-Speller that received its name from the event-related potential (ERP) P300 which is elicited by the speller (oddball) paradigm. Several researchers reported that it is not only the P300 but also other ERPs that a...

We examine the success of a common LDA classification algorithm for P300 Speller BCI systems on an individual basis. Experiments performed on 16 subjects (7 with severe motor impairment, 9 with no motor impairment) indicate that the P300 Speller should, on average, work in both client and neurotypical populations. Here, we find that the N100 of an averaged ERP, a measure associated with selecti...

The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct b...

BACKGROUND Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) generate weak SSVEP with a monitor and cannot use harmonic frequencies, whereas P300-based BCIs need multiple stimulation sequences. These issues can decrease the information transfer rate (ITR). NEW METHOD In this paper, we introduce a novel hybrid SSVEP-P300 speller that generates dual-frequency S...

In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a ...

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix sp...