A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids

Hwang, Han-Jeong; Ferreria, Valeria Y.; Ulrich, Daniel; Kilic, Tayfun; Chatziliadis, Xenofon; Blankertz, Benjamin; Treder, Matthias S.

doi:10.1038/srep15890

Cited by 20 publications

(19 citation statements)

References 59 publications

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“…Previous studies introduced non-visual BCIs such as auditory and tactile BCI for patients with ocular dysfunction404142434445464748. Recently, the EC BCI paradigm was newly proposed based on the SSVEP28 or event-related potentials (ERP)29. However, it is not suitable to use fast task-relevant response for NIRS-based BCI since it cannot be clearly detected by means of NIRS because of its inherent hemodynamic delay49.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, many BCI studies are focusing on on-line BCI and on-line implementation is essential to confirm the practical usability of BCI paradigm295455. We evaluated the feasibility of NIRS-based EC BCI as an alternative of conventional BCI paradigms and performed off-line analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, they would have difficulties in using conventional BCI paradigms requiring normal or moderate visual functions for command selection and/or feedback. Two recent EEG-based BCI studies showed the feasibility of EC BCI paradigms2829. One study used the steady-state visual evoked potential (SSVEP) paradigm28 and the other used the visual P300 paradigm29.…”

mentioning

confidence: 99%

“…Two recent EEG-based BCI studies showed the feasibility of EC BCI paradigms2829. One study used the steady-state visual evoked potential (SSVEP) paradigm28 and the other used the visual P300 paradigm29. Both studies showed acceptable classification results.…”

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confidence: 99%

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Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

Shin

Müller

Hwang

2016

Sci Rep

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We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

Shin

Müller

Hwang

2016

Sci Rep

Self Cite

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“…In neuroscience, machine learning has been applied as a statistical tool for investigating brain activity or structure, and as a computational model emulating brain activity [10]. In the first capacity, it has mostly been employed in the form of classifiers for applications such as multivariate analysis [7], classification of clinical data [13], and brain-computer interfaces [11]. Multivariate analysis of neuroimaging data poses several challenges, two of which are addressed here.…”

Section: Introductionmentioning

confidence: 99%

Improving SNR and Reducing Training Time of Classifiers in Large Datasets via Kernel Averaging

Treder

2018

Brain Informatics

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Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data.

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Mind, the Gap

McCullagh¹

2016

Communications in Computer and Information Science

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Training is needed to exercise our most important organ: the brain, exploit its full potential and sustain it for our later years. Does a gap exist in our capacity for learning, a gap between what our brain could potentially achieve and what we are currently prepared to accept? The 'prototype' follows an ambitious undergraduate Computer Science student as he gets drawn into psychophysiological experiments that explore brain-training, involving image recognition, cognition, subliminal delivery, and imagined movement. Such technology has great potential for promoting and assessing learning and possibly exploring under performance or dysfunctional learning. However, as with all technology that enhances the human, there is potential for unintended use that we should be mindful of.

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A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids

Cited by 20 publications

References 59 publications

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

Improving SNR and Reducing Training Time of Classifiers in Large Datasets via Kernel Averaging

Mind, the Gap

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