This paper proposes and creates a new generalised view towards BCI with its related application and recent challenges that can notice through the field of application of BCI e.g. designing the new local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The classifier may be embedded in a portable braincomputer interface called ABI, which has been evalua...

Background: Surface Electromyography (SEMG) signal has used in monitoring muscle activities. It has been widely applied in many areas, such as body member prosthesis, noise cancellation for braincomputer interface, and robotics. The SEMG acquisition method for collecting the signal with low-noise has extensively investigated in the last decade. The objective of this study is to review the recen...

In this paper, we discuss the formalized approach for generating and estimating symbols (and alphabets), which can be communicated by the wide range of non-verbal means based on specific user requirements (medium, priorities, type of information that needs to be conveyed). The short characterization of basic terms and parameters of such symbols (and alphabets) with approaches to generate them a...

Using steady-state visually evoked potential (SSVEP) in braincomputer interface (BCI) systems is the subject of a lot of research. One of the most popular and widely used detection method is using a power spectral density analysis (PSDA). Lately there have been some new methods emerging, one of them is using canonical correlation analysis (CCA) which seems to have some promising improvements an...

Brain-computer interfaces (BCIs) have been examined in the field of bio-medical engineering. This braincomputer interface method is very useful for the people who are suffered by some nervous disorder to control or operate the external devices. EEG dataset are acquired and these signals are processed for identifying the brain thoughts to control the device. Here we proposed the method for the c...

Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment – e.g. game score, completion time, etc. – in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is implicit and subjective – e.g. human preferences for certain AI behavior – in order...

The paper describes experiences from implementing a simple snake game, which can be controlled by the user’s brainwaves using the NeuroSky mindset. The NeuroSky mindset is an inexpensive Brain-Computer Interface (BCI) device allowing developers to process EEG signals that can be used to control a computer. The BCI opens for new ways for humans to interact with computers, and can be used for man...

Most new digital musical interfaces have evolved upon the intuitive idea that there is a causality between sonic output and physical actions. Nevertheless, the advent of braincomputer interfaces (BCI) now allows us to directly access subjective mental states and express these in the physical world without bodily actions. In the context of an interactive and collaborative live performance, we pr...

The aim of this paper is to explore a new multimodal Computer Aided Design (CAD) platform based on braincomputer interfaces and touch based systems. The paper describes experiments and algorithms for manipulating geometrical objects in CAD systems using touch-based gestures and movement imagery detected though brain waves. Gestures associated with touch based systems are subjected to ambiguity ...

Motor imagery attenuates EEG α and β rhythms over sensorimotor cortices. These amplitude changes are most successfully captured by the method of Common Spatial Patterns (CSP) and widely used in braincomputer interfaces (BCI). BCI methods based on amplitude information, however, have not incoporated the rich phase dynamics in the EEG rhythm. This study reports on a BCI method based on phase sync...