Chapter 8 High-frequency oscillatory activities during selective attention in humans

Ozaki, Isamu; Yaegashi, Yukoh; Baba, Masayuki; Hashimoto, Isao

doi:10.1016/s1567-424x(09)70012-1

Cited by 3 publications

(1 citation statement)

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“…This result is consistent with PFC being involved in the global attentional system (Dirlikov et al, 2015 ; Keune et al, 2015 ) regardless of the modalities of input information. Another study reported that attention in humans was associated with high frequency oscillations of approximately 350 Hz that occurred in frontal and centro-parietal regions in response to somatosensory stimuli (Ozaki et al, 2006 ). Several hypotheses have been proposed to explain the role of high-frequency oscillations in attention.…”

Section: Feature Extraction For Visual-attention Bcismentioning

confidence: 99%

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

et al. 2016

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We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention.

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Section: Feature Extraction For Visual-attention Bcismentioning

confidence: 99%