AttentivU

Kosmyna, Nataliya; Morris, Caitlin; Nguyễn, Thanh Hải; Zepf, Sebastian; Hernández, Javier; Maes, Pattie

doi:10.1145/3342197.3344516

Cited by 13 publications

(6 citation statements)

References 52 publications

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“…It would be interesting to see if the choice of different subsets of features could be derived and if it could influence the performance of different classifiers, in line with DiLascio et al [54]. Additionally, based on the emerging work mentioned in Challenge 1 , other physiological modalities like EOG could be used to deal with fatigue either offline (research paper using JINS Meme for example, [55]), as well as to measure it in real time combined with EEG by Kosmyna et al, [55].…”

Section: Discussion Challenges Future Work and Limitationsmentioning

confidence: 99%

AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning

Kosmyna¹,

Maes²

2019

Sensors

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Information about a person’s engagement and attention might be a valuable asset in many settings including work situations, driving, and learning environments. To this end, we propose the first prototype of a device called AttentivU—a system that uses a wearable system which consists of two main components. Component 1 is represented by an EEG headband used to measure the engagement of a person in real-time. Component 2 is a scarf, which provides subtle, haptic feedback (vibrations) in real-time when the drop in engagement is detected. We tested AttentivU in two separate studies with 48 adults. The participants were engaged in a learning scenario of either watching three video lectures on different subjects or participating in a set of three face-to-face lectures with a professor. There were three conditions administrated during both studies: (1) biofeedback, meaning the scarf (component 2 of the system) was vibrating each time the EEG headband detected a drop in engagement; (2) random feedback, where the vibrations did not correlate or depend on the engagement level detected by the system, and (3) no feedback, when no vibrations were administered. The results show that the biofeedback condition redirected the engagement of the participants to the task at hand and improved their performance on comprehension tests.

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Section: Discussion Challenges Future Work and Limitationsmentioning

confidence: 99%

AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning

Kosmyna¹,

Maes²

2019

Sensors

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“…The glasses also consist of two EOG electrodes located at the nosepads and one EEG reference electrode located at the Fpz location ( Figure 7 ). We refer the reader to [ 10 , 11 ] to learn more about the glasses’ architecture.…”

Section: Bci Implementation and Resultsmentioning

confidence: 99%

“…EEG and EOG can represent internal cognitive states and external subconscious behaviors, respectively. These two modalities contain complementary information and when combined can be used to construct more robust estimation models of vigilance, attention, and engagement based on state-of-the-art methods [ 10 ]. In the context of our task, these sensors can provide the required information, as well as support real-time, closed-loop interventions when needed.…”

Section: Bci Implementation and Resultsmentioning

confidence: 99%

“…On the client side, the user interacts with the brain–computer interface (BCI) device (in the case of this paper, the AttentivU device (Cambridge, MA, USA) [ 10 , 11 ], to be introduced in depth in Section 4 of this paper), via a mobile application that communicates with the device using the Bluetooth low energy (BLE) protocol. The user’s mobile device (Phone B) communicates with another mobile phone that controls the Spot robot in order to enable agency, manipulation, navigation, and ultimately assistance for the end user.…”

Section: Infrastructure Of Ddogmentioning

confidence: 99%

“…Additionally, we want to ensure that both (1) and (2) parts of the system are mobile, wireless, and autonomous. We thus chose to use a pair of wireless glasses with EEG/EOG electrodes [ 10 , 11 ], embedded in the frame of the glasses. We found this form-factor to be promising and novel, as well as it to be the least intrusive means possible for the user.…”

Section: Introductionmentioning

confidence: 99%

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A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration

Kosmyna,

Hauptmann,

Hmaidan

2023

Sensors

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Coupling brain–computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one’s brain activity only. These types of systems might be of a particular interest for people with locked-in syndrome (LIS) or amyotrophic lateral sclerosis (ALS) because they can benefit from communicating with robotic assistants using brain sensing interfaces. In this proof-of-concept work, we explored how a wireless and wearable BCI device can control a quadruped robot—Boston Dynamics’ Spot. The device measures the user’s electroencephalography (EEG) and electrooculography (EOG) activity of the user from the electrodes embedded in the glasses’ frame. The user responds to a series of questions with YES/NO answers by performing a brain-teaser activity of mental calculus. Each question–answer pair has a pre-configured set of actions for Spot. For instance, Spot was prompted to walk across a room, pick up an object, and retrieve it for the user (i.e., bring a bottle of water) when a sequence resolved to a YES response. Our system achieved at a success rate of 83.4%. To the best of our knowledge, this is the first integration of wireless, non-visual-based BCI systems with Spot in the context of personal assistant use cases. While this BCI quadruped robot system is an early prototype, future iterations may embody friendly and intuitive cues similar to regular service dogs. As such, this project aims to pave a path towards future developments in modern day personal assistant robots powered by wireless and wearable BCI systems in everyday living conditions.

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