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Abstract
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.
Highlights
The visual system in both human and non-human organisms transforms complex input information into robust neural representation of the visual world
This study showed that approximately 20% of PMd reflected the orientation of selective spatial attention, which could be disengaged from the other spatial variables
The largest subpopulation of prefrontal cortex (PF) neurons was linked to attention, not to working memory, which indicated that PF has a major contribution to selective spatial attention
Summary
The visual system in both human and non-human organisms transforms complex input information into robust neural representation of the visual world. The algorithm can evaluate how well the brain signals match certain requirements, and generate a feedback based on the difference Such feedback can be used to improve neural function in patients: patients observe their own brain activity in real time, and learn to self-regulate this activity in order to bring it to normal state. A good understanding of neurophysiology of attention is required to extract attentional signals from neural recordings and dissociate them from the other ongoing activities in the brain (Sanei and Chambers, 2008) Notwithstanding these difficulties, visual-attention based BCI systems have been already developed and applied to ADHD (Christiansen et al, 2014; Heinrich et al, 2014; Holtmann et al, 2014a,b; Micoulaud-Franchi et al, 2014; Steiner et al, 2014b). We cover the current state of the art and future challenges in this research
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