Wireless Brain Computer Interface for Smart Home and Medical System

Jafri, Syed Rehan Abbas; Hamid, Tehreem; Mahmood, Rabia; Alam, Muhammad Asjad; Rafi, Talha; Haque, M. Z. Ul; Munir, Muhammad Wasim

doi:10.1007/s11277-018-5932-x

Cited by 55 publications

(32 citation statements)

References 14 publications

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“…In this research work, a wireless electroencephalogram (EEG) based BP monitoring system is designed based on the modification of our prior research work [11]. The stationary part of WBCI was intended to control home utilities at steady state while the mobile part controls handy medical devices such as infrared therapeutic belt and digital wrist BP monitor [11]. Firstly, an EEG headset senses brain signals, which remotely sent the signals to the stationary component and subsequently sent to the portable part.…”

Section: Methodsmentioning

confidence: 99%

“…The wrist BP monitor was used as one of the medical devices to measure BP reading, which was not precise. The overall system was intended to be used for aged and paralyzed patients with respect to routine activities [11]. The biggest challenge to the prior system is the accuracy of BP device used in that system since many clinical decisions are strictly based on the BP measurements with the least possible error [12].…”

Section: Introductionmentioning

confidence: 99%

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Wireless Electroencephalography Based Blood Pressure Monitoring

Jafri¹,

Shahid²,

Shamim³

et al. 2018

IJET

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The number of cardiac patients and aged individuals are at a rise all around the world. Taking care of such individuals is a major challenge these days. In many cases, these patients require special care and regular monitoring of vital signs like blood pressure (BP). Focusing a prevalent idea of wireless brain-computer interface (WBCI), an innovative research work is considered to meet essential routine monitoring of BP for cardiac patients and aged people without any reliance. The research framework involves the use of wireless electroencephalogram (EEG) headset to control wrist BP and arm BP monitors to determine accurate BP readings in the proposed system. An Android application "Smart Home Monitor" is developed that screens the information from the headset. The research framework is tested on ten individuals to examine the precision in BP readings from two different BP monitors. Results specify that both upper arm blood pressure readings i.e. Systolic BP readings (SBP = 119.6 ±5.1 mmHg) and Diastolic BP (DBP = 79.5 ±7.4 mmHg) were found to be better than the wrist BP readings (SBP = 128.2 ±11.7 mmHg and DBP = 83.6 ±10.3 mmHg). This examination assessed that the designed system empowers the framework to be reliable, remote and compact.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wireless Electroencephalography Based Blood Pressure Monitoring

Jafri¹,

Shahid²,

Shamim³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…In recent years, brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP) has attracted a lot of attention due to its high information transfer rate (ITR) and little user training [1]- [3]. It could be used in smart home appliances [4], [5], disability assistance [6]- [9], human-computer interaction [10]- [12], games and entertainment [13], [14] and other fields. SSVEP is evoked by repetitive stimulus with a constant frequency on the central retina, SSVEP-BCI could detect which stimuli the user is…”

Section: Introductionmentioning

confidence: 99%

SSVEP Stimulus Layout Effect on Accuracy of Brain-Computer Interfaces in Augmented Reality Glasses

Zhao

Liu

et al. 2020

IEEE Access

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Steady-state visual evoked potentials-based brain-computer interfaces (SSVEP-BCI) has the advantage of high information transfer rate (ITR) and little user training, and it has a high application value in the field of disability assistance and human-computer interaction. Generally SSVEP-BCI requires a personal computer screen (PC) to display several repetitive visual stimuli for inducing the SSVEP response, which reduces its portability and flexibility. Using augmented reality (AR) glasses worn on the head to display the repetitive visual stimuli could solve the above drawbacks, but whether it could achieve the same accuracy as PC screen in the case of reduced brightness and increased interference is unknown. In current study, we firstly designed 4 stimulus layouts and displayed them with Microsoft HoloLens (AR-SSVEP) glasses, comparison analysis showed that the classification accuracies are influenced by the stimulus layout when the stimulus duration is less than 3s. When the stimulus duration exceeds 3s, there is no significant accuracy difference between the 4 layouts. Then we designed a similar experimental paradigm on PC screen (PC-SSVEP) based on the best layout of AR. Classification results showed that AR-SSVEP achieved similar accuracy with PC-SSVEP when the stimulus duration is more than 3s, but when the stimulus duration is less than 2s, the accuracy of AR-SSVEP is lower than PC-SSVEP. Brain topological analysis indicated that the spatial distribution of SSVEP responses is similar, both of which are strongest in the occipital region. Current study indicated that stimulus layout is a key factor when building SSVEP-BCI with AR glasses, especially when the stimulation time is short. INDEX TERMS Steady-state visual evoked potentials (SSVEP), brain-computer interfaces (BCI), augmented reality (AR), optical see-through (OST), human-computer interaction. FIGURE 1. AR-SSVEP experiment environment, hardware devices include EEG acquisition and HoloLens.

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“…The proposed system uses NeuroSky BCI headband to acquire neural signals against an eye blink to turn on a desired he appliance. Guger et al [9] suggested non-invasive Subject Dependent P300 stimulus based on BCI provides the subject with a stimulus and records his reaction as an input. P300 BCIs rely on selective attention to visual stimuli.…”

Section: Introductionmentioning

confidence: 99%

Smart Home Control for Disabled Using Brain Computer Interface

Qin¹,

Nasir²,

Huq³

et al. 2020

IJIE

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Electroencephalography (EEG) based smart home control system is one of the major applications of Brain Computer Interface (BCI) that allows disabled people to maximize their capabilities at home. A Brain Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. In this project, the scope includes Graphical User Interface (GUI) acts as a control and monitoring system for home appliances which using BCI as an input. Hence, NeuroSky MindWave headset is used to detect EEG signal from brain. Furthermore, a prototype model is developed using Raspberry Pi 3 Model B+, 4 channels 5V relay module, light bulb and fan. The raw data signal from brain wave is being extracted to operate the home appliances. Besides, the results agree well with the command signal used during the experiment. Lastly, the developed system can be easily implemented in smart homes and has high potential to be used in smart automation.

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Wireless Brain Computer Interface for Smart Home and Medical System

Cited by 55 publications

References 14 publications

Wireless Electroencephalography Based Blood Pressure Monitoring

Wireless Electroencephalography Based Blood Pressure Monitoring

SSVEP Stimulus Layout Effect on Accuracy of Brain-Computer Interfaces in Augmented Reality Glasses

Smart Home Control for Disabled Using Brain Computer Interface

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