The brain-computer interface (BCI) community started to consider games as potential applications while the games community started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. In this paper, we propose a preliminary BCI games framework that we constructed with respect to the research conducted in both the BCI and t...

The abstract presents a tactile pinpressure stimulus-based brain-computer interface (BCI) paradigm. The 3x3 pressure pin matrix stimulus patterns are presented to the users in an oddball paradigm allowing for brainwave “aharesponses” generation to attended targets. Our research hypothesis is confirmed with the results with five users performing online BCI experiments.

Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brainwaves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of suc...

Minimum energy combination (MEC) is a widely used method for frequency recognition in steady state visual evoked potential based BCI systems. Although it can reach acceptable performances, this method remains sensitive to noise. This paper introduces a new technique for the improvement of the MEC method allowing ameliorating its Anti-noise capability. The Empirical mode decomposition (EMD) and ...

An EEG-based brain-computer system for automating home appliances is proposed in this study. Brain-computer interface (BCI) system provides direct pathway between human brain and external computing resources or external devices. The system translates thought into action without using muscles through a number of electrodes attached to the user’s scalp. The BCI technology can be used by disabled ...

A non-invasive brain-computer Interface (BCI) offers a direct interaction between the human brain and an external device without any surgical risk. Currently, electroencephalography (EEG) has been the most studied non-invasive interface because of its portability and low set-up cost. However, susceptibility to electromagnetic noise has been a barrier to promoting practical non-invasive BCIs in ...

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 ...