Hybrid P300-Based Brain-Computer Interface to Improve Usability for People With Severe Motor Disability: Electromyographic Signals for Error Correction During a Spelling Task

Riccio, Angela; Holz, Elisa Mira; Aricò, Pietro; Leotta, Francesco; Aloise, Fabio; Desideri, Lorenzo; Rimondini, Matteo; Kübler, Andrea; Mattia, Donatella; Cincotti, Febo

doi:10.1016/j.apmr.2014.05.029

Cited by 51 publications

(55 citation statements)

References 13 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, two or more brain imaging methods can be combined in a hybrid BCI such that brain signals from an EEG and fNIRS system in order to take advantage of each brain imaging technology [41,72–75]. Some research studies applied other physiological signals, such as electromyography (EMG) [32,76], electrooculogram (EOG) [77,78] and electrocardiography (ECG) [31,79] to brain signal(s) to address common limitations of brain signals, such as lower amplitude, non-stationarity, and vulnerability to muscle artifact. In addition, external signals can be added to support BCI systems including eye-tracking [71,80], a gyroscope [81], a position sensor [82], and a joystick [83,84].…”

Section: Study 1: Taxonomy Of Hybrid Bcismentioning

confidence: 99%

“…For example, EEG and fNIRS could be used complementary to one another to measure brain signal features, because EEG has high temporal resolution and low spatial resolution while fNIRS has high spatial resolution and low temporal resolution [41,72–75]. A multi-physiological acquisition method has the advantage of higher classification accuracy due to not only the application of the classification result with additional physiological signals, but also the high signal-to-noise ratio of EMG and EOG signal [32,76–78]. Contrary to the combinations of physiological signals, external inputs including joysticks, eye trackers, and gyroscopes are directly utilized as a controller by modulating hand or body movements for directional applications such as a navigation [59], robot control [91] and game control [83].…”

Section: Study 1: Taxonomy Of Hybrid Bcismentioning

confidence: 99%

See 1 more Smart Citation

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%

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

“…Briefly, a BCI is defined as “ a system that measures Central Nervous System (CNS) activity and converts it into artificial output that replaces, restores, enhances or improves natural CNS output and thereby changes the ongoing interactions between the CNS and its external or internal environment” (Wolpaw and Wolpaw, 2012). Such definition summarizes the progresses of the scientific community in this field during the last decades, since at the moment the possibility of using the BCI systems outside the laboratories (Aloise et al, 2010; Blankertz et al, 2010; Aricò et al, 2011; Riccio et al, 2015; Schettini et al, 2015), by developing applications in everyday life is not just a theory but something very close to real applications (Zander et al, 2009; Blankertz et al, 2010; Aricò et al, 2016). This technology has been defined passive Brain-Computer Interface (pBCI).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Aricò¹,

Borghini²,

Flumeri³

et al. 2016

Front. Hum. Neurosci.

Self Cite

173

143

View full text Add to dashboard Cite

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

show abstract

“…The results that have been obtained in the past several years have demonstrated the possibility of performing complex tasks by relying on noninvasive EEG systems, highlighting the potential of BCI as an assistive technology (AT) option (Kleih et al, 2011;McFarland et al, 2008;Riccio et al, 2015;Schettini et al, 2015). Similarly, encouraging results have been generated with noninvasive BCI systems in stroke rehabilitation RamosMurguialday et al, 2013).…”

Section: Introductionmentioning

confidence: 82%

Interfacing brain with computer to improve communication and rehabilitation after brain damage

Riccio

Pichiorri

Schettini

et al. 2016

Progress in Brain Research

View full text Add to dashboard Cite

Hybrid P300-Based Brain-Computer Interface to Improve Usability for People With Severe Motor Disability: Electromyographic Signals for Error Correction During a Spelling Task

Cited by 51 publications

References 13 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

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Interfacing brain with computer to improve communication and rehabilitation after brain damage

Contact Info

Product

Resources

About