Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

P300, Steady State Visual Evoked Potentials, And Hybrid Paradigms For A Brain Computer Interface Speller

The goal of this research was to evaluate and compare two types of brain computer interface (BCI) systems, P300 and steady state visually evoked potentials (SSVEP), as spelling paradigms and combine them as a hybrid approach. There were pilot experiments performed for designing the parameters of the SSVEP spelling paradigm including peak detection for different range of frequencies, placement of LEDs, design of the SSVEP stimulus board, and window time for the SSVEP peak detection processing. The next experiment was to evaluate the SSVEP spelling paradigm. Six subjects participated in the task. The accuracy of each frequency and average accuracy for each subject were considered. The second experiment was designed to compare the performance and accuracy of SSVEP, P300, and the combination of both paradigms as a simultaneous task. Ten subjects were considered for performing this experiment. Overall the average accuracy of the SSVEP spelling paradigm was 80.00 % and higher than the P300 spelling paradigm average accuracy which was 72.50 %, and both of the spelling paradigms have better accuracy than the hybrid paradigm with the average accuracy of 64.39 %

User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

The effects of mental training on brain computer interface performance with distractions

The overall success of a brain computer interface (BCI) is largely dependent on the features used to make decisions. Noise in the electroencephalography (EEG) increases the difficulty of acquiring meaningful features. Previous literature suggests teaching subjects meditation and relaxation techniques may improve features relevant to BCI operation. The purpose of this study was to investigate performance on several cognitive protocols for both individuals who use meditation techniques and those who do not use these techniques. Both groups were given a motor imagery based BCI protocol, a P300 speller BCI, a verbal learning task, and an N-back test. No significant difference in performance was found between meditation and control groups. Our research does suggest however, significant differences for the P300 and motor imagery protocols may be found if a larger group (\u3e20 subjects per class) is recruited

Improving Brain-Computer Interface Performance: Giving the P300 Speller Some Color.

Individuals who suffer from severe motor disabilities face the possibility of the loss of speech. A Brain-Computer Interface (BCI) can provide a means for communication through non-muscular control. Current BCI systems use characters that flash from gray to white (GW), making adjacent character difficult to distinguish from the target. The current study implements two types of color stimulus (grey to color [GC] and color intensification [CI]) and I hypotheses that color stimuli will; (1) reduce distraction of nontargets (2) enhance target response (3) reduce eye strain. Online results (n=21) show that GC has increased information transfer rate over CI. Mean amplitude revealed that GC had earlier positive latency than GW and greater negative amplitude than CI, suggesting a faster perceptual process for GC. Offline performance of individual optimal channels revealed significant improvement over online standardized channels. Results suggest the importance of a color stimulus for enhanced response and ease of use

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

© 2017 The Author(s). Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s