Bi-directional channel modeling for implantable UHF–RFID transceivers in brain–computer interface applications

Ajrawi, Shams Al; Bialek, Hayden; Sarkar, Mitali; Rao, Ramesh R.; Ahmed, Syed Hassan

doi:10.1016/j.future.2018.03.036

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…The smaller size of an ultrasonic transducer is an added advantage for applications such BCI. In Opnet, the ultrasonic technology parameters [29] were used to transmit data. The simulation is performed using the same procedure as described for the UHF and UWB.…”

Section: Numerical and Simulation Resultsmentioning

confidence: 99%

“…This article considers a BCI system, where N T transceivers on the brain surface to collect and transmit the EEG brain signals, and N R transceivers are placed on the scalp to acquire the transmitted signals. The sensors used are semiactive, and [26], [27], ultra-wideband RFID (UWB-RFID) [28], [29], and ultrasonic technology mimicking the neural dusts by modifying the superframe structure [15], [30], [31].…”

Section: Bci System Modelmentioning

confidence: 99%

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Hybrid MAC Protocol for Brain–Computer Interface Applications

Ajrawi

Al-Dweik

Rao

et al. 2021

IEEE Systems Journal

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Brain-computer interface (BCI) can permit individuals to use their thoughts as the sole means to control objects such as smart homes and robots. While BCI is a promising interdisciplinary tool, researchers are confronting network lifetime as an obstacle to further development. Furthermore, the medium access control (MAC) protocol is the bottleneck of the network reliability. There are many standards for MAC protocols that can be utilized for productive and dependable transmission by altering the control parameters. Modifying these parameters is another source of concern due to the scarcity in knowledge about its effect. In addition, there is no instrument accessible to receive and actualize these parameters on transmitters embedded inside the cerebrum. In this article, we give the transmission instrument to both ultrahigh-frequency (UHF) radio frequency identification (RFID) and ultra-wideband (UWB) signals for multiple transmitters and ultrasonic technology mimicking the neural dusts by modifying the superframe structure. In this article, a hybrid MAC protocol is proposed, and the results show that the traffic received can be increased by 700% for UHF-RFID and more than 100% for UWB and ultrasonic technology. Comparative results for wireless channel MAC protocols using these different transmission techniques are discussed in terms of network delay, data dropped, traffic sent, and traffic received. Index Terms-Brain-computer interfaces (BCIs), data dropped, delay, frequency-division multiple access (FDMA), medium access control (MAC), time-division multiple access (TDMA), ultrahighfrequency radio frequency identification (UHF-RFID), ultrasonic, ultra-wideband (UWB).

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Section: Numerical and Simulation Resultsmentioning

confidence: 99%

Section: Bci System Modelmentioning

confidence: 99%

Hybrid MAC Protocol for Brain–Computer Interface Applications

Ajrawi

Al-Dweik

Rao

et al. 2021

IEEE Systems Journal

Self Cite

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“…The most important criterion for this is the introduction and application of wireless methods by Radio frequency identification (RFID) and Delay Tolerant Network (DTN) technology. Combining the method we have developed, the RFID and DTN processes allows us to create an even more complex brain machine interface [30,31,32].…”

Section: Discussionmentioning

confidence: 99%

Measurement system with real time data converter for conversion of I2S data stream to UDP protocol data

Vizvári

Tóth

Sari

et al. 2020

Heliyon

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A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a "low" temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain.

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“…BCIs interpret neural activity (Berger et al 2007), contrasting with Computer-Brain Interfaces that stimulate neural activity (Allison, Wolpaw, and Wolpaw 2007) and with bi-directional interfaces (Al Ajrawi et al 2018;Lajoie et al 2017;London et al 2008;Mavoori et al 2005;O'Doherty et al 2011). This research focuses on BCIs, although clearly there is a range of ethical implications arising from CBIs and bi-directional interfaces (for example, the effects of stimulating neural activity on autonomy and agency).…”

Section: Brain-computer Interfaces 30mentioning

confidence: 99%

Privacy and brain-computer interfaces

Wahlstrom¹,

Fairweather²,

Ashman³

2016

SIGCAS Comput. Soc.

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Brain-Computer Interfaces (BCIs) interpret neural activity, applying it to the control of external devices. As BCIs approach market viability, ethical implications come under consideration. This paper identifies potential privacy disruptions. BCI literature is reviewed in order to identify a BCI typology likely to support a privacy analysis. The typology describes the active, reactive, passive and hybrid types of BCI and, where possible, includes examples that are further classified as existing, prospective or speculative. A review of privacy theory supports an analysis that juxtaposes privacy theory and BCI technologies. The analysis finds that while all four types of BCI have potential for disrupting privacy, disruptions are more likely to arise from the use of reactive, passive and hybrid BCIs. Limitations and directions for future research close the paper.

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Bi-directional channel modeling for implantable UHF–RFID transceivers in brain–computer interface applications

Cited by 14 publications

References 13 publications

Hybrid MAC Protocol for Brain–Computer Interface Applications

Hybrid MAC Protocol for Brain–Computer Interface Applications

Measurement system with real time data converter for conversion of I2S data stream to UDP protocol data

Privacy and brain-computer interfaces

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