Controlling of smart home system based on brain-computer interface

Gao, Qiang; Zhao, Xuewen; Yu, Xianxiang; Song, Yu; Wang, Zhe

doi:10.3233/thc-181292

Cited by 27 publications

(11 citation statements)

References 24 publications

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“…It performs mainly in two aspects, controlling household appliances and undertaking housework. SSVEP-based BCI offers people the possibility to recognize various commands and control corresponding devices in their houses by watching different stimuli [33]. By means of the quick response technology QR code, Abdul et al [34] designed an augmented reality smart glasses to control items in the environment, such as lights, coffee machines and elevators, by focusing on different SSVEP stimuli displayed on the glasses.…”

Section: Healthcare Applicationsmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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Section: Healthcare Applicationsmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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“…Also, many researchers have presented BCI systems in the context of "smart" homes for controlling home appliances. [34][35][36][37][38][39][40][41][42][43] The development of BCI systems that would allow disabled people to control almost every device in their home-from a wheelchair to any electronic appliance-would have a tremendous effect on their life quality. In this paper, we present a case study, as a proof of concept, for an elevator BCI system that could be part of a complete "smart" home BCI system.…”

Section: The Conceptmentioning

confidence: 99%

Residential buildings with brain-computer interface functionality: An elevator case study

Chatziparasidis¹,

Sfampa²

2021

Building Services Engineering Research and Technology

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Brain–computer interfaces (BCI) are systems that use signals recorded from the brain to enable communication and control applications. One of the most important applications of BCI technology is that enables people who are severely paralyzed by amyotrophic lateral sclerosis, brainstem stroke, or other disorders to communicate, operate computer programs, or even control numerous devices. Moreover, elevators are probably the best option for disabled persons to expand their access and mobility within a house or a building. In this study, a prototype application is presented, together with an experimental setup of a BCI system that attempts to control an elevator. Practical application Many researchers are dealing with BCI systems that give the possibility to disabled people to control a variety of devices from wheelchairs to different home appliances, using the signals of their brain and forming a smart home services framework. This work comes to support this effort by presenting a case study, as a proof of concept, for an elevator BCI system that could be part of a complete “smart” home BCI system. The presented experimental setup proves that elevators with BCI functionalities are practically feasible and in an affordable cost, and that they could be a significant element within a “smart” residential building.

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“…Notably, many studies that utilize electroencephalography (EEG) have accomplished home use in a variety of applications [ 34 – 38 ]; however, EEG-based BCIs lack the spatial resolution captured by more invasive techniques [ 11 ]. Moreover, the use of non-invasive technologies, such as EEG, for BCI control signals can complicate set up procedures (e.g., appropriate electrode placement) limiting the subject’s independence and the ease of BCI use at home.…”

Section: Introductionmentioning

confidence: 99%

Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury

Davis

Meschede-Krasa

Cajigas

et al. 2022

J NeuroEngineering Rehabil

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Objective The objective of this study was to develop a portable and modular brain–computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). Background BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. Methods The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject’s wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. Results Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject’s caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. Conclusions The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015

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Controlling of smart home system based on brain-computer interface

Cited by 27 publications

References 24 publications

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Residential buildings with brain-computer interface functionality: An elevator case study

Design-development of an at-home modular brain–computer interface (BCI) platform in a case study of cervical spinal cord injury

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