The Human Factors and Ergonomics of P300-Based  Brain-Computer Interfaces

Powers, Jack; Bieliaieva, Kateryna; Wu, Shuohao; Nam, Chang S.

doi:10.3390/brainsci5030318

Cited by 31 publications

(19 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Articles were sought out from five major search engines including IEEE Xplore, PubMed, Engineering Village, Web of Science, and Scopus, since those engines cover engineering and medical topics, as well as a broad-spectrum perspective [70]. Eligibility and exclusion criteria follow.…”

Section: Study 1: Taxonomy Of Hybrid Bcismentioning

confidence: 99%

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

et al. 2017

View full text Add to dashboard Cite

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

show abstract

Section: Study 1: Taxonomy Of Hybrid Bcismentioning

confidence: 99%

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Wolpaw et al [11] reported the first use of sensorimotor rhythms (SMRs) for BCI control. SMRs are oscillations (i.e., mu (8)(9)(10)(11)(12) and beta (18-30 Hz)) recorded over sensorimotor cortices that change in amplitude with movement, imagined movement, or preparation for movement [12]. People can learn to control SMR amplitudes to move a cursor to hit targets on a video screen or perform other computer-based tasks.…”

Section: Eeg-based Bcismentioning

confidence: 99%

EEG-Based Brain-Computer Interfaces

Lehtonen¹

2019

View full text Add to dashboard Cite

Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-ofprinciple; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.

show abstract

“…The fact that the P300 potential reflects attention, rather than simply gaze direction, implies that this BCI could be used by people who lack eye-movement control [8]. Many research groups are further developing P300-based BCIs [9]. Several groups have explored BCIs that use auditory rather than visual stimuli; these would be useful for people with visual impairments [10].…”

Section: Eeg-based Bcismentioning

confidence: 99%

EEG-based brain–computer interfaces

McFarland

Wolpaw

2017

Current Opinion in Biomedical Engineering

206

View full text Add to dashboard Cite

Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.

show abstract

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

Cited by 31 publications

References 76 publications

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

EEG-Based Brain-Computer Interfaces

EEG-based brain–computer interfaces

Contact Info

Product

Resources

About