Leveraging Neurophysiological Information to Augment Interpretation of Responses to Vulnerable Robot Behaviors

Holloman, Amanda K.; Egbert, William; Stegman, Pierce; Cioli, Nicholas; Crawford, Chris

doi:10.1109/hri.2019.8673290

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…To solve this problem, a few researchers started to process EEG signals by neural networks [70,71,72]. But neural networks require a lot of dataset and computing power and despite this the neural networks are increasingly being used in various fields of activity, robot control [73,74] to identify various mental illnesses [51]. Over the past few years, the number of studies that have involved machine learning and deep learning models to recognize the EEG signal has significantly increased.…”

Section: Introductionmentioning

confidence: 99%

Review Dry and Non-contact EEG Electrodes for 2010-2021 years

Rakhmatulin

Gan²

2021

SSRN Journal

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The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain or some of its individual areas using a special sensor/electrode. This method is used for the treatment and diagnosis of various diseases. The use of wet electrodes in this case does not seem viable, for several well-known reasons. As a result of this, a detailed analysis of modern EEG sensors developed over the past few years is carried out, which will allow researchers to choose this type of sensor more carefully and, as a result, conduct their research more competently. Due to the absence of any standards in the production and testing of dry EEG sensors, the main moment of this manuscript is a detailed description of the necessary steps for testing a dry electrode, which will allow researchers to maximize the potential of the sensor in the various type of research.

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