2014
DOI: 10.1007/s00146-014-0545-8
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Brain–computer interfaces and dualism: a problem of brain, mind, and body

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Cited by 8 publications
(6 citation statements)
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“…Artificial intelligence (AI) and machine learning (ML) are applied in many areas [1] such as medical technologies [2,3], big data [4], various neuro linked technologies [5][6][7][8][9][10][11][12][13][14][15], autonomous cars, drones, As such, disabled people have a stake in AI/ML advancements and how they are governed. Furthermore, disabled people have many distinct roles to contribute to AI/ML advancement discussions in general and in particular to AI/ML ethics and governance discussions, such as therapeutic and non-therapeutic user, knowledge producer, knowledge consumer, influencer of the discourses, and victims.…”
Section: Introductionmentioning
confidence: 99%
“…Artificial intelligence (AI) and machine learning (ML) are applied in many areas [1] such as medical technologies [2,3], big data [4], various neuro linked technologies [5][6][7][8][9][10][11][12][13][14][15], autonomous cars, drones, As such, disabled people have a stake in AI/ML advancements and how they are governed. Furthermore, disabled people have many distinct roles to contribute to AI/ML advancement discussions in general and in particular to AI/ML ethics and governance discussions, such as therapeutic and non-therapeutic user, knowledge producer, knowledge consumer, influencer of the discourses, and victims.…”
Section: Introductionmentioning
confidence: 99%
“…Artificial intelligence (AI) and machine learning (ML) are applied to many scientific and technological endeavors such as personalized medicine (Feng, Badgeley, Mocco, & Oermann, 2018), medical diagnostics (Ilyasova, Kupriyanov, Paringer, & Kirsh, 2018), big data (André et al, 2018), virtual reality (Falconer & Ortega, 2018), neuroimaging (Feng et al, 2018), brain computer interface (BCI) (Lee, 2016), artificial brain (Buttazzo, 2001), deep brain stimulation (Camara et al, 2015;Catherwood, Finlay, & McLaughlin, 2016), cochlear implants (Meeuws et al, 2017), transcranial magnetic stimulation (Erguzel et al, 2015), gaming (Shubik, 1960), autono-mous cars (Gonzalez et al, 2016), drones for military purposes (Sharkey, 2011), and various assistive technologies.…”
Section: Introductionmentioning
confidence: 99%
“…With the market for wearable consumer products currently valued at $12.3 billion and projected to increase to $30.7 billion by 2021, consumers will increasingly have access to different health metrics, such as cardiac activity and caloric output, within reach. The consumer demand for better wearable devices and more interesting metrics subsequently drove the development of low-cost, wearable neural systems that use electroencephalography (EEG), a non-invasive, highly temporal, imaging technique that monitors electrical activity of the brain through electrodes that are placed on the scalp (Mak and Wolpaw, 2009; van Gerven et al, 2009; McFarland and Wolpaw, 2011; Nicolas-Alonso and Gomez-Gil, 2012; Daly and Huggins, 2015; Lee, 2016). As wearable, low-cost EEG systems become more prevalent and available to consumers, there is a need to assess the potential applications and capabilities of these low-cost EEG systems in measuring neural activity.…”
Section: Introductionmentioning
confidence: 99%