Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain control movement-evoking stimulation. Common methods use spike-sorting and require frequent calibration high computational complexity. Furthermore, most applications of closed-loop sti...

Most of the previous work on brain-computer interfaces (BCIs) using P300 has been focused on feature extraction and classification algorithms to achieve high performance for the communication between the brain and the computer. While significant progress has been made in such lower layer of the BCI system, the issues in the higher layer have not been addressed sufficiently. Existing P300-based ...

Abstract Due to its non-invasiveness and easiness implement, EEG signals decoding are in base of most based brain computer interfaces (BCI) studies. Given the non-stationary nature these signals, a preprocessing phase is needed. An interesting idea perform use spatial covariance matrices. In last years, matrices was extensively used electroencephalography (EEG) signal processing filtering for M...

Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will ...

Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes o...

The Brain-Computer Interfaces can successfully help the healthy and disabled users, in performing various activities in their day to day life. But still such high expectations are yet to be fulfilled by the existing BCI designs due to their restricted reliability and less understanding of the brain mechanisms used in it. Brain-computer interface (BCI) systems based on the steady-state visual ev...

In recent work, we have provided evidence that fronto-parietal γ-range oscillations are a cause of within-subject performance variations in brain-computer interfaces (BCIs) based on motor-imagery. Here, we explore the feasibility of using neurofeedback of fronto-parietal γpower to induce a mental state that is beneficial for BCI-performance. We provide empirical evidence based on two healthy su...

This paper presents FuRIA, a trainable feature extraction algorithm for non-invasive Brain-Computer Interfaces (BCI). FuRIA is based on inverse solutions and on the new concepts of fuzzy Region Of Interest (ROI) and fuzzy frequency band. FuRIA can automatically identify the relevant ROI and frequency bands for the discrimination of mental states, even for multiclass BCI. Once identified, the ac...

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize...

Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to ...