Feasibility of approaches combining sensor and source features in brain–computer interface

Ahn, Minkyu; Hong, Jun Hee; Jun, Sung Chan

doi:10.1016/j.jneumeth.2011.11.002

Cited by 27 publications

(27 citation statements)

References 29 publications

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“…Thanks to their efforts, we have seen advances that may bring widespread use of BCI to the fore. Hardware companies have released both inexpensive and sophisticated EEG headsets [18] and researchers have reported the development of new signal processing algorithms [27,42] that reduce calibration time [43–46] and improve accuracy [47–51]. Looking at enhancements in performance, for example, a BCI spelling system was able to type about 0.5 characters per minute in 1999 [52], while ten years later a German group demonstrated performance exceeding 7 characters per minute [53].…”

Section: Discussionmentioning

confidence: 99%

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

Ahn

Lee

Choi

et al. 2014

Sensors

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158

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In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

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Section: Discussionmentioning

confidence: 99%

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

Ahn

Lee

Choi

et al. 2014

Sensors

Self Cite

158

View full text Add to dashboard Cite

show abstract

“…Finally, recent studies have demonstrated that beam forming can be used in online brain–computer interfaces (BCI) translating neuromagnetic signals into control signals of external devices 50 . Such paradigms allow for learned control of brain activity of specific cortical and subcortical areas 51 .…”

Section: Discussionmentioning

confidence: 99%

In vivo assessment of human brain oscillations during application of transcranial electric currents

et al. 2013

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Brain oscillations reflect pattern formation of cell assemblies’ activity, which is often disturbed in neurological and psychiatric diseases like depression, schizophrenia and stroke. In the neurobiological analysis and treatment of these conditions, transcranial electric currents applied to the brain proved beneficial. However, the direct effects of these currents on brain oscillations have remained an enigma because of the inability to record them simultaneously. Here we report a novel strategy that resolves this problem. We describe accurate reconstructed localization of dipolar sources and changes of brain oscillatory activity associated with motor actions in primary cortical brain regions undergoing transcranial electric stimulation. This new method allows for the first time direct measurement of the effects of non-invasive electrical brain stimulation on brain oscillatory activity and behavior.

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“…The source localization procedure is contingent on a single regularization parameter: the prior sparsity level. Sparsity is a common assumption, employed in estimating ill-posed inverse solutions, and widely applied in EEG imaging [12,39,28,42]. In EEG, the sparsity assumption is motivated by the apparently sparse focal nature of brain activation [16].…”

Section: Forward Model Representationmentioning

confidence: 99%

“…However, the macroscopic EEG signal is generally believed to originate from well-localized gray matter sources [8,9], and therefore makes full 3D spatial reconstruction of the dipole source distribution a valuable imaging modality. The source reconstruction process has been shown to reduce non-brain artifact signal components [10]; it allows incorporation of spatial a priori information from functional activation databases [11]; and it generally leads to improved interpretability [12,13] by reducing the blurring effects of volume conduction.…”

Section: Introductionmentioning

confidence: 99%

Data-driven forward model inference for EEG brain imaging

2016

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Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.

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Feasibility of approaches combining sensor and source features in brain–computer interface

Cited by 27 publications

References 29 publications

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

In vivo assessment of human brain oscillations during application of transcranial electric currents

Data-driven forward model inference for EEG brain imaging

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