Towards Internet-of-Things for Wearable Neurotechnology

Elmalaki, Salma; Demirel, Berken Utku; Taherisadr, Mojtaba; Stern‐Nezer, Sara; Lin, Jack J.; Faruque, Mohammad Abdullah Al

doi:10.1109/isqed51717.2021.9424364

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…Ahuja et al [75] proposed "EduSense", a system that captures both audio and video streams of the instructor and students to analyze effective instruction, including hand raises, body pose, body accelerometry, and speech acts and provides analytical feedback to the instructor. While the education sector has already taken technological leaps over the last decades with the introduction of smart classroom concept [25], [75], [55], efficient real-time measurement of the learning outcomes that incorporate the student's mental state has to be considered [14], [54].…”

Section: Human-in-the-loop Approaches For Learningmentioning

confidence: 99%

“…First, humans can learn through techniques other than rule-based concept learning. Second, although we needed to use a high accuracy EEG cap (Figure 1b) to exploit the feasibility of classifying the learning events, we need to consider more socially acceptable wearable devices as we envision human-in-the-loop IoT learning system [14].…”

Section: Explanation-based Concept Learningmentioning

confidence: 99%

“…In addition, recent research in the human-in-the-loop IoT systems has shown that humans have intrinsic inter-and intra-variation. Hence, we can not have one model-fits-all approach in designing real-time adaptations to improve the human learning [15], [14], [76], [5].…”

Section: Introductionmentioning

confidence: 99%

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ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Taherisadr¹,

Faruque²,

Elmalaki³

2023

Preprint

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Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals.Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by 26%. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.

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Section: Human-in-the-loop Approaches For Learningmentioning

confidence: 99%

Section: Explanation-based Concept Learningmentioning

confidence: 99%

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ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Taherisadr¹,

Faruque²,

Elmalaki³

2023

Preprint

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“… 11 , 13 Thus, research advances in social neuroscience are likely to revolutionize different fields such as entertainment, communication, education, healthcare, and social embedding, among others. 14 Recently, researchers have started to explore brain activity from a collective perspective, using a contemporary approach, known as hyperscanning. 15 …”

Section: Introductionmentioning

confidence: 99%

Brain-to-brain communication during musical improvisation: a performance case study

Ramírez-Moreno,

Cruz-Garza,

Acharya

et al. 2023

F1000Res

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Understanding and predicting others' actions in ecological settings is an important research goal in social neuroscience. Here, we deployed a mobile brain-body imaging (MoBI) methodology to analyze inter-brain communication between professional musicians during a live jazz performance. Specifically, bispectral analysis was conducted to assess the synchronization of scalp electroencephalographic (EEG) signals from three expert musicians during a three-part 45 minute jazz performance, during which a new musician joined every five minutes. The bispectrum was estimated for all musician dyads, electrode combinations, and five frequency bands. The results showed higher bispectrum in the beta and gamma frequency bands (13-50 Hz) when more musicians performed together, and when they played a musical phrase synchronously. Positive bispectrum amplitude changes were found approximately three seconds prior to the identified synchronized performance events suggesting preparatory cortical activity predictive of concerted behavioral action. Moreover, a higher amount of synchronized EEG activity, across electrode regions, was observed as more musicians performed, with inter-brain synchronization between the temporal, parietal, and occipital regions the most frequent. Increased synchrony between the musicians' brain activity reflects shared multi-sensory processing and movement intention in a musical improvisation task.

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“…Context-aware systems provide personalized services that are adaptive according to user context and surrounding environments. These pervasive systems have enabled a multitude of applications in several IoT sectors including smart cities [39], health care [35], smart classrooms [13,57], and automotive systems [3,16,37]. However, these enhanced capabilities come at the expense of privacy weaknesses [14,24,53,55] and sometimes lack of fairness in context-adaptation [12].…”

Section: Introductionmentioning

confidence: 99%

VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT

Elmalaki,

Ho,

Alzantot

et al. 2022

Preprint

Self Cite

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Personalized IoT adapt their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapt to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract user's private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. The rest of this paper is devoted to introducing VindiCo 1 , a software mechanism designed to detect and mitigate possible SpyCon. Being a new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or app behavior are not adequate to detect SpyCon. Therefore, VindiCo proposes a novel information-based detection engine along with several mitigation techniques to restrain the ability of the detected SpyCon to extract private information. By having a general detection and mitigation engines, VindiCo is agnostic to the inference algorithm used by SpyCon. Our results show that VindiCo reduces the ability of SpyCon to infer user context from 90.3% to the baseline accuracy (accuracy based on random guesses) with negligible execution overhead 2 .

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Towards Internet-of-Things for Wearable Neurotechnology

Cited by 15 publications

References 43 publications

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System

Brain-to-brain communication during musical improvisation: a performance case study

VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT

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