Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients

Shrivastava, Rajesh; Paul, Varghese; Menon, Varun G.; Jacob, Sunil; Vinod, P.

doi:10.1109/jiot.2019.2951405

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…Improvements and standardizations in the realm of defining these systems is also an important step, as the definitions provided in the literature are inconsistent. Studies focus too much on their application for transmitting motor information, as in Mashat et al (2017), overly specify attributes such as wireless transmission, as in Rajesh et al (2020), or only specify the inclusion of a BCI and CBI portion and very little else, as in Rao et al (2014) and Li and Zhang (2016). More cohesion in the field as to what type of B2BI is being presented would allow for quicker communication of applications for these devices.…”

Section: Discussion B2bi Systemsmentioning

confidence: 99%

“…Of the studies that employed EEG, either motor imagery (MI) or steady state visually evoked potentials (SSVEP) were measured by the BCI. MI EEG was used as a simple method of generating a binary signal, based on event related desynchronization (ERD) between the left and right hemispheres during right and left hand/feet movements, in Grau et al (2014), Rao et al (2014), and Rajesh et al (2020). MI EEG also functioned as a direct translation, where the imagined movement of a hand corresponded to a similar action on the receiving end (right hand movement begets right turn, etc.)…”

Section: Bci Methodologymentioning

confidence: 99%

“…Phosphenes, or tMS stimulation of the visual cortex to produce artifacts in a person's field of view, were used as a visual indicator of a binary choice (1 or 0, yes or no, left or right) in Grau et al (2014), Stocco et al (2015), and Jiang et al (2019). The remainder of applications of tMS were for stimulating the human motor cortex either to press a button (Rao et al, 2014;Rajesh et al, 2020) or directly move a subject's limb (Mashat et al, 2017). Though less common, tFUS is also capable of non-invasively modulating neural activity.…”

Section: Cbi Methodologymentioning

confidence: 99%

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Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface

et al. 2021

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This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.

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Section: Discussion B2bi Systemsmentioning

confidence: 99%

Section: Bci Methodologymentioning

confidence: 99%

Section: Cbi Methodologymentioning

confidence: 99%

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Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface

et al. 2021

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“…If the measured tilt passes a particular threshold, a similar emergency message will be given to the caregiver via a wireless transmitter. The secure communication is ensured between paralyzed persons and caregivers using a double encrypted NTSA algorithm [26]. The material used for the development is carbon fiber, so that it can easily replicate various body movements with ease.…”

Section: Proposed System a System Architecturementioning

confidence: 99%

An Adaptive and Flexible Brain Energized Full Body Exoskeleton With IoT Edge for Assisting the Paralyzed Patients

Jacob

Alagirisamy

Menon³

et al. 2020

IEEE Access

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The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients.

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“…These protocols have an essential role in energy conservation as they control the main sources of energy wastage, i.e., packet collision, overhearing, control packet overhead, and idle listening. MAC protocols are classified as either schedule-based [13], [14], [15] or contention-based [16], [17], [18]. In the case of contention-based protocols, e.g., Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA), the devices compete with each other to access the transmission channels for data communication.…”

Section: Introductionmentioning

confidence: 99%

A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System

Yang

Jan

Menon³

et al. 2020

IEEE Access

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The emergence of the Internet of Things (IoT) has revolutionized our digital and virtual worlds of connected devices. IoT is a key enabler for a wide range of applications in today's world. For example, in smart healthcare systems, the sensor-embedded devices monitor various vital signs of the patients. These devices operate on small batteries, and their energy need to be utilized efficiently. The need for green IoT to preserve the energy of these devices has never been more critical than today. The existing smart healthcare approaches adopt a heuristic approach for energy conservation by minimizing the duty-cycling of the underlying devices. However, they face numerous challenges in terms of excessive overhead, idle listening, overhearing, and collision. To circumvent these challenges, we have proposed a cluster-based hierarchical approach for monitoring the patients in an energy-efficient manner, i.e., green communication. The proposed approach organizes the monitoring devices into clusters of equal sizes. Within each cluster, a cluster head is designated to gather data from its member devices and broadcast to a centralized base station. Our proposed approach models the energy consumption of each device in various states, i.e., idle, sleep, awake, and active, and also performs the transitions between these states. We adopted an analytical approach for modeling the role of each device and its energy consumption in various states. Extensive simulations were conducted to validate our analytical approach by comparing it against the existing schemes. The experimental results of our approach enhance the network lifetime with a reduced energy consumption during various states. Moreover, it delivers a better quality of data for decision making on the patient's vital signs.

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Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients

Cited by 24 publications

References 23 publications

Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface

Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface

An Adaptive and Flexible Brain Energized Full Body Exoskeleton With IoT Edge for Assisting the Paralyzed Patients

A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System

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