EEG-based Brain-computer Interface for Automating Home Appliances

Alshbatat, Abdel Ilah; Vial, Peter James; Premaratne, Prashan; Tran, Le Chung

doi:10.4304/jcp.9.9.2159-2166

Cited by 19 publications

(13 citation statements)

References 22 publications

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“…Taking this use of EEG one step forward multiple articles [15][16][17][18][19][20][21] discuss integrating EEG with the Internet of Things (IOT) to control different devices in a smart home using protocols like Message Queue Telemetry Transport (MQTT) and Extensible Messaging and Presence Protocol (XMPP). These interfaces can further be used to improve healthcare and the general living standard of differently-abled people and in-turn make everyone's life easier.…”

Section: Review Of Eeg For Bci Applicationsmentioning

confidence: 99%

“…A similar algorithm is applied to control the fan, however in that case, changes in a combination of frequency and amplitude; in the Beta range of frequency (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), meeting the threshold result in the change of state of the fan. Based on further analysis of the EEG signal in various frequency ranges, multiple such IOT devices can be controlled.…”

Section: Algorithm Work-flowmentioning

confidence: 99%

See 1 more Smart Citation

Design of an EEG Acquisition System for Embedded Edge Computing

Rai¹,

Thakur²,

Mane³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The human brain is one of the most complex machines on the planet. Being the only method to get real-time data with high temporal resolution from the brain makes EEG a highly sought upon signal in the neurological and psychiatric domain. However, recent developments in this field have made EEG more than just a tool for medical professionals. The decreasing size and increasing complexity of EEG acquisition systems have brought it out of the lab and into the field where it is used for varied applications like neurofeedback, person recognition and other recreational activities. Amalgamation of the EEG signal with new developing standards of Industry 4.0 to control basic IOT devices using edge computing techniques marks the next step in the design and development our low-cost yet robust Brain Computer Interface (BCI); which is just one of the many applications that a versatile and well-built EEG acquisition system can be used for.

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Section: Review Of Eeg For Bci Applicationsmentioning

confidence: 99%

Section: Algorithm Work-flowmentioning

confidence: 99%

Design of an EEG Acquisition System for Embedded Edge Computing

Rai¹,

Thakur²,

Mane³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…In [10], the authors built a platform that uses IoT paradigms in connecting EEG headsets with home devices using the cloud. Other extensive work in [11] proposed a system for controlling home applications using EMOTIV EPOC and embedded module. A third interesting approach is demonstrated in [12], where the authors used Neuroky headset for signal acquisition connecting it to an Arduino using a Bluetooth module.…”

Section: Related Workmentioning

confidence: 99%

Home Automation using EMOTIV: Controlling TV by Brainwaves

Zaki¹,

Alquraini²,

Sheltami³

2018

JUSPN

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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 where it is received by a Raspberry Pi microcontroller. The microcontroller sends the appropriate response based on what is received from the client, which turns on or off a TV. This application will have a great potential especially for people with Amyotrophic Lateral Sclerosis (ALS) or special needs in general.

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“…In this paper, we propose a novel in-home occupational therapy environment, which incorporates off-the-shelf EEG, EMG, eye tracking sensors, smart home IoT sensors [42], and kinematic gesture tracking non-invasive sensors to support forward and inverse kinematic actions. We have developed a 3D game environment in which the sensory data from the brain, muscles, joint range of motion and eye positions are fed to a digital avatar.…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-Based Non-Invasive Cyber-Physical Occupational Therapy Framework: BCI Perspective

et al. 2019

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Although ElectroEncephaloGram (EEG) signals allow subjects suffering from neuromuscular disorders to interface their brains with the cyber-physical world, occupational therapy can be enhanced with the introduction of further modalities better assist the disabled person. In this paper, we propose an in-home occupational therapy environment, which leverages a rich set of occupational therapy-related activity recognition modalities, namely, EEG signals to understand brain activity, ElectroMyoGram (EMG) signals for muscle activity, gesture-tracking sensors for forward and inverse kinematics activities, and smart home appliance control sensors. To support a wide variety of disabled people's in-home occupational therapy, we have incorporated both selective attention and motor imagery processes for mapping a mental command with that of an occupational therapy-related command within a serious game environment. To attain higher accuracy and to avoid a higher number of false positives, a subject is first recommended to use a selective attention-based serious game in which a digital avatar of the subject acting as a model therapist will guide the therapy session. Once familiar with the generation of proper motor imagery, an advanced user can use self-paced motor imagery signals to perform occupational therapy activities within the serious game environment. The occupational therapy consists of a serious game environment in which smart home appliances are mapped with therapeutic activities through forward and inverse kinematics. The therapy data has been secured through blockchain and off-chain-based distributed repositories. The test results show the viability of using the framework in a clinical environment. INDEX TERMS Brain computer interface, blockchain, off-chain, serious games, digital virtual avatar.

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EEG-based Brain-computer Interface for Automating Home Appliances

Cited by 19 publications

References 22 publications

Design of an EEG Acquisition System for Embedded Edge Computing

Design of an EEG Acquisition System for Embedded Edge Computing

Home Automation using EMOTIV: Controlling TV by Brainwaves

A Blockchain-Based Non-Invasive Cyber-Physical Occupational Therapy Framework: BCI Perspective

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