Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information

Morioka, Hiroshi; Kanemura, Atsunori; Morimoto, Satoshi; Yoshioka, Toshinori; Oba, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

doi:10.1016/j.neuroimage.2013.12.035

Cited by 55 publications

(47 citation statements)

References 52 publications

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“…As compared with our findings of decoding covert attentional shift to subsets of external stimuli, there are other recent studies decoding visuospatial attention directly from the intrinsically driven brain activity patterns evoked by attention6970717273. As our study demonstrates the possibility of seamlessly decoding human top-down processes, if a further refined grid-shaped line array is developed and combined with a more elaborated classification method, a recent study74 provides advances in extending previous studies of decoding visuospatial attention6970717273, which were limited to decoding up to four discrete locations/classes.…”

Section: Discussionsupporting

confidence: 74%

“…As our study demonstrates the possibility of seamlessly decoding human top-down processes, if a further refined grid-shaped line array is developed and combined with a more elaborated classification method, a recent study74 provides advances in extending previous studies of decoding visuospatial attention6970717273, which were limited to decoding up to four discrete locations/classes. The authors found a neuronal signature of direct two-dimensional access to the spatial location of covert attention in macaque prefrontal cortex74.…”

Section: Discussionmentioning

confidence: 77%

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Decoding of top-down cognitive processing for SSVEP-controlled BMI

Min

Dähne

Ahn

et al. 2016

Sci Rep

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We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 77%

Decoding of top-down cognitive processing for SSVEP-controlled BMI

Min

Dähne

Ahn

et al. 2016

Sci Rep

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“…The reliability of VBMEG has been confirmed by both computer simulation [14], [25] and experiment [26]–[32].…”

Section: Methodsmentioning

confidence: 68%

Revealing Time-Unlocked Brain Activity from MEG Measurements by Common Waveform Estimation

et al. 2014

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Brain activities related to cognitive functions, such as attention, occur with unknown and variable delays after stimulus onsets. Recently, we proposed a method (Common Waveform Estimation, CWE) that could extract such brain activities from magnetoencephalography (MEG) or electroencephalography (EEG) measurements. CWE estimates spatiotemporal MEG/EEG patterns occurring with unknown and variable delays, referred to here as unlocked waveforms, without hypotheses about their shapes. The purpose of this study is to demonstrate the usefulness of CWE for cognitive neuroscience. For this purpose, we show procedures to estimate unlocked waveforms using CWE and to examine their role. We applied CWE to the MEG epochs during Go trials of a visual Go/NoGo task. This revealed unlocked waveforms with interesting properties, specifically large alpha oscillations around the temporal areas. To examine the role of the unlocked waveform, we attempted to estimate the strength of the brain activity of the unlocked waveform in various conditions. We made a spatial filter to extract the component reflecting the brain activity of the unlocked waveform, applied this spatial filter to MEG data under different conditions (a passive viewing, a simple reaction time, and Go/NoGo tasks), and calculated the powers of the extracted components. Comparing the powers across these conditions suggests that the unlocked waveforms may reflect the inhibition of the task-irrelevant activities in the temporal regions while the subject attends to the visual stimulus. Our results demonstrate that CWE is a potential tool for revealing new findings of cognitive brain functions without any hypothesis in advance.

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“…A subsequent EEG study, using a very similar experimental design reports an average performance of 75% (chance being at 50%) based on the alpha power of the recorded signals (Treder et al, 2011). Morioka et al (2014), report a slightly higher average decoding performance (79%) using near-infrared spectroscopy (NIRS) as prior information for the analysis of the EEG signals. It is however not clear if this improved performance results from using the NIRS prior information or is due to the fact that an exogenous cue was used instead of an endogenous cue.…”

Section: Using Attention-related Signals To Control a Brain–machine Imentioning

confidence: 99%

Selective visual attention to drive cognitive brainâ€“machine interfaces: from concepts to neurofeedback and rehabilitation applications

Åstrand

Wardak

Hamed

2014

Front. Syst. Neurosci.

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Brain–machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.

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Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information

Cited by 55 publications

References 52 publications

Decoding of top-down cognitive processing for SSVEP-controlled BMI

Decoding of top-down cognitive processing for SSVEP-controlled BMI

Revealing Time-Unlocked Brain Activity from MEG Measurements by Common Waveform Estimation

Selective visual attention to drive cognitive brainâ€“machine interfaces: from concepts to neurofeedback and rehabilitation applications

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