2018
DOI: 10.5383/juspn.10.01.004
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Home Automation using EMOTIV: Controlling TV by Brainwaves

Abstract: In this paper, our goal is to prove the possibility of controlling a home device by solely using neural pattern recognition interface captured by Emotiv EPOC. The neuroheadset EPOC is a personal interface for human interaction with computer through the acquisition of electrical signals produced by the brain, via techniques of electroencephalography (EEG), in order to identify thoughts, feelings and facial expressions in real time. The pattern is then fed to a client, which communicates it to the server side wh… Show more

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Cited by 12 publications
(12 citation statements)
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“…Participants were seated in a comfortable condition and were asked to keep as motionless as possible during the entire procedure, to minimize possible signal interference due to movement. We have used both Emotiv and MUSE EEG headsets as suggested by Zaki et al [6]. We have used a Node.js and AngularJS based framework to access data from the Emotiv EPOC brain sensor or the open dataset available from Emotiv.…”
Section: Methodsmentioning
confidence: 99%
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“…Participants were seated in a comfortable condition and were asked to keep as motionless as possible during the entire procedure, to minimize possible signal interference due to movement. We have used both Emotiv and MUSE EEG headsets as suggested by Zaki et al [6]. We have used a Node.js and AngularJS based framework to access data from the Emotiv EPOC brain sensor or the open dataset available from Emotiv.…”
Section: Methodsmentioning
confidence: 99%
“…The signals portray a disabled person's mental desires to do an action [5]. A BCI intercepts these brain electrophysiological signals through an invasive or noninvasive computing hardware and software and finally maps each distinct brain signal with a certain action [6]. In the case of OT, the BCI is designed to understand mapping between the brain signals identified by the BCI and the corresponding occupational therapy commands [7].…”
Section: Introductionmentioning
confidence: 99%
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“…In addition, Shedeed et al developed "Brain EEG signal processing for controlling a robotic arm," in Ain Shams and Benha University in Egypt in 2008 where they achieved an error rate of 9% for 3 actions [3]. Zaki et al also implemented a "Home Automation using EMOTIV: Controlling TV by Brainwaves," in 2018 at King Fahad University of Petroleum and Minerals in Dhahran, Saudi Arabia [4]. Additionally, Hamzah et al also developed "Special Issue EEG signal classification to detect left and right command," a system based on Artificial Neural Network (ANN) algorithm in 2017 [1].…”
Section: Related Workmentioning
confidence: 99%
“…After surveying lung cancer patients, the authors of [75] concluded that QoL data should be studied at every visit for each patient and in-between visits. Since OT is intended to allow ADL independently [76], OT QoL monitoring metrics such as the type, length, and frequency of therapeutic exercises, and change in the difficulty level or course of activities is recommended to be personalized [77,78,79,80,81,82,83,84,85,86,87,88,89]. Moreover, data privacy, confidentiality, and integrity can be assured by leveraging the recent advancement in blockchain and off-chain-based decentralized solutions, which guarantees the availability and scalability of OT data, proper end-to-end encryption, a digital wallet with secure cryptographic public/private keys, and high-speed transaction overlays [90,91].…”
Section: System Designmentioning
confidence: 99%