We report on an extended robot control application of a contact-less and airborne ultrasonic tactile display (AUTD) stimulus-based brain-computer interface (BCI) paradigm, which received last year The Annual BCI Research Award 2014. In the award winning human communication augmentation paradigm the six palm positions are used to evoke somatosensory brain responses, in order to define a novel co...

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected in...

Brain-Computer Interface (BCI) community has focused the majority of its research efforts on signal processing and machine learning, mostly neglecting the human in the loop. Guiding users on how to use a BCI is crucial in order to teach them to produce stable brain patterns. In this work, we explore the instructions and feedback for BCIs in order to provide a systematic taxonomy to describe the...

In this paper we introduce the concept of Brain-Computer Interface (BCI) inhibitor, which is meant to standby the BCI until the user is ready, in order to improve the overall performance and usability of the system. BCI inhibitor can be defined as a system that monitors user’s state and inhibits BCI interaction until specific requirements (e.g. brain activity pattern, user attention level) are ...

We investigate various forms of face-to-face and multiparty interac‐ tions in the context of potential brain-computer interface interactions (BCI). BCI has been employed in clinical applications but more recently also in domestic and game and entertainment applications. This paper focusses on multi-party game applications. That is, BCI game applications that allow multiple users and different B...

EEG-based brain computer interface (BCI) provides a new communication channel between the human brain and a computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present a new modification on classic "Peak Picking" to make a better detection for a specific pattern in EEG. The new method shows a significant improvement in P300 detection which is a commo...

A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user ...

Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable. The development of a practical BCI posed the challenge of classifying single-trial EEG with a small training set. In this letter, we addressed this problem by employing a series of signal processing and machine learning approaches to ...

Abstract— The BCI research has now witnessed incredible expansion using invasive and noninvasive methods but especially the use of Electroencephalogram (EEG) signals (method of non-invasive BCI) has attained a lot of significance. The applications of EEG based BCI ranges from medicine to entertainment. In this paper, a general Electro-Encephalogram (EEG) based BCI system is discussed. Thus deve...

Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BC...