This study provides a comprehensive test of the head–related impulse response (HRIR) to an auditory spatial speller brain–computer interface (BCI) paradigm, including a comparison with a conventional virtual headphone–based spatial auditory modality. Five BCI–naive users participated in an experiment based on five Japanese vowels. The auditory evoked potentials obtained produced encouragingly g...

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 ...

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...

This study reports on a head related impulse response (HRIR) application to an auditory spatial brain-computer interface (BCI) speller paradigm. Six experienced and five BCInaive users participated in an experimental spelling set up based on five Japanese vowels. Obtained auditory evoked potentials resulted with encouragingly good and stable P300-responses in online BCI experiments. Our case st...

A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hipp...

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...

The BCI Speller is a machine that flashes different letters on a screen and collects EEG data from the viewer, then analyzes the data to determine what letter the viewer was concentrating on. This allows a user to spell words without any body movements, which is useful for cases of heavy paralysis where speech is inhibited. The neurological event that this machine searches for is the P300, a pu...

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 report on the feasibility of the Error-Related Potential (ErrP) integration in a particular type of Brain-Computer Interface (BCI) called the P300 Mind Speller. With the latter, the subject can type text only by means of his/her brain activity without having to rely on speech or muscular activity. Hereto, electroencephalography (EEG) signals are recorded from the subject’s sca...

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...