In this paper we review classification algorithms used to design BrainComputer Interface (BCI) systems based on ElectroEncephaloGraphy (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI. PACS...

This paper, investigates the use of a 3D setting for BrainComputer Interface (BCI) by implementing the 3D interface for the P300-Speller device. The 3D configurations were implemented using two different approaches which are called Natural 3D and Parallel 2D. The theoretical analysis concerning these two approaches are provided considering the modifications in speed, accuracy, and capacity. The...

In this paper we review classification algorithms used to design BrainComputer Interface (BCI) systems based on ElectroEncephaloGraphy (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI. PACS...

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a braincomputer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with...

We consider an EEG-based, wireless braincomputer interface (BCI) with which subjects can “mind spell” text on a computer screen. The application is based on the detection of the P300 event-related potential (ERP). The frequency range for preprocessing the EEG recordings, and the location and length of the time interval after stimulus onset, are selected with respect to the classification accura...

This paper presents on-going research into the creation of performance and compositional tools using a BrainComputer Music Interface (BCMI). The research demonstrates the suitability of the SSVEP (Steady-State Visual Evoked Potentials) technique of generating brainwave information to cognitively control music. Furthermore, it considers the practical implications of using brainwaves in music, an...

As demonstrated in our recent work “Improving the accuracy in person lineups and identification via braincomputer interfaces“ we used brain computer interface technology to distinguish between known and unknown persons faces when presented to a witness as a set of stimuli. The classifiers are built using the BCI2000 framework, a multipurpose research software. In this paper we will extend this ...

Steady-state visually evoked potentials (SSVEP) can be elicited by a large variety of stimuli. To the best of our knowledge, the size and shape effect of stimuli has never been investigated in the literature. We study the relationship between the visual parameters (size and shape) of the stimulation and the resulting brain response. A tentative physiological interpretation is proposed and the p...

Artefacts are noises introduced to the electroencephalogram’s (EEG) signal by not central nervous system (CNS) sources of electric fields inside and outside subject’s body. The artefacts impede the analysis of the signal and should be handled properly. The most common and characteristic kind of artefacts is the electrooculographic (EOG) ones, especially subject’s eye-blinking artefacts. In this...

Motivated by the particular problems involved in communicating with “locked-in” paralysed patients, we aim to develop a braincomputer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on ...