This work presents the findings of a research study which investigated the possibility of automating parameter selection for an EEG-based P300-driven Brain-Computer Interface (BCI) using a Genetic Algorithm (GA). Although this approach showed marked advantages over current practices, the GA approach required lengthy execution times which render it infeasible for online utilisation. The GA metho...

Brain-computer interfaces (BCI) can allow disabled people to drive a wheel chair, just by imagining movement. Therefore, present day BCIs use training data, recorded during four different conditions, to calibrate a classifier. This, however, causes jerky behavior while abruptly switching between discrete states. We propose a cost-sensitive support vector approach for estimating two-dimensional ...

To explore whether the brain contains pattern differences in rock–paper–scissors (RPS) imagery task, this paper attempts to classify task using fNIRS and deep learning. In study, we designed an RPS with a total duration of 25 min 40 s, recruited 22 volunteers for experiment. We used acquisition device (FOIRE-3000) record cerebral neural activities these participants task. The time series classi...

Brain-computer interfaces (BCI) come with a lot of issues, such as delays, bad recognition, long training times, and cumbersome hardware. Gamers are a large potential target group for this new interaction modality, but why would healthy subjects want to use it? BCI provides a combination of information and features that no other input modality can offer. Current research is mostly still focusin...

OBJECTIVE Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. APPROACH We introduce a new approach to brain-computer interfacing based ...

Brain-computer interfaces (BCIs) are devices primarily intended to allow agents move prosthetic limbs, wheelchairs, and other mechanisms by forming intentions or performing certain mental actions. In this article, I illustrate how the use of BCIs leads two unique unrecognised problems moral luck. short, it seems that who depend upon for bodily movement (henceforth ‘BCI agents’) may end up deser...

Compared to invasive Brain-Computer Interfaces (BCI), non-invasive BCI systems based on Electroencephalogram (EEG) signals have not been applied successfully for precisely timed control tasks. In the present study, however, we demonstrate and report on the interaction of subjects with a real device: a pinball machine. Results of this study clearly show that fast and well-timed control well beyo...

The investigation of innovative Human–Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain–Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amy...

Revealing the information between similar patterns of brain for a real motor task and its imaginary equivalent can be means to clarify movement intentions help improve Brain Computer Interfaces (BCI)s. This paper uses spectral coherence assess functional interactions neural regions engaged in an imagined arm task. Magnitude squared values were calculated two specific bands Electroencephalogram ...

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face...