Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene

Abstract

The availability of accurate and reliable dry sensors for electroencephalography (EEG) is vital to enable large-scale deployment of brain–machine interfaces (BMIs). However, dry sensors invariably show poorer performance compared to the gold standard Ag/AgCl wet sensors. The loss of performance with dry sensors is even more evident when monitoring the signal from hairy and curved areas of the scalp, requiring the use of bulky and uncomfortable acicular sensors. This work demonstrates three-dimensional micropatterned sensors based on a subnanometer-thick epitaxial graphene for detecting the EEG signal from the challenging occipital region of the scalp. The occipital region, corresponding to the visual cortex of the brain, is key to the implementation of BMIs based on the common steady-state visually evoked potential paradigm. The patterned epitaxial graphene sensors show efficient on-skin contact with low impedance and can achieve comparable signal-to-noise ratios against wet sensors. Using these sensors, we have also demonstrated hands-free communication with a quadruped robot through brain activity.

Feedback Mechanism for Blind and Visually Impaired: A Review

“…These challenges often arise during ingress and egress, where individuals may need to navigate unfamiliar terrain or even with independent navigation. (Faisal et al, 2023) and Neuralink's BCI implant in right (Waltz, 2020). (Caraiman et al, 2017) -High (causes cognitive overload if auditory and tactical interfaces used are insufficient to communicate information) (Elli et al, 2014), Med -High (Shih et al, 2012)]…”

2D Materials in Flexible Electronics: Recent Advances and Future Prospectives

“…Integrating data collected from various wearable sensors with other IoT-based sensors and systems can provide a holistic understanding of an individual’s health, behavior, and environment. For example, bioelectronic sensors can measure the ECG , and EEG , of the patient, while electrochemical and physical sensors can measure temperature, humidity, air quality, and the movement of the patient. Furthermore, the development of sophisticated applications and services, including various healthcare monitoring and treatment applications, can be accelerated by fully integrated wearable sensors. , Thus, fully integrated 2D material-based wearable sensors have the potential to provide a more comprehensive insight into various physiological and environmental parameters in a more user-friendly and efficient way.…”

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

“…The system used image tracking, Kinect camera, and a P300 to detect which actions the patients want to target in their domestic environment. This system has a potential for everyday use because it is not limited to preprogrammed actions and uses object detection to respond to stimuli in the patient's [29] and (F) [8]-the implants used to control limbs and exoskeleton using MI BCI paradigm; (E) [30]-the most recent paper to date, featuring a hybrid MI + SSVEP + EMG system; (B) [16], (D) [31], and (G) [32], all using the same cap for different robotic use cases as well as the MI BCI paradigm; (C) [33] featuring the P300 interface; and finally, (H) [34], the only paper to the best of our knowledge featuring a quadruped robot and using the SSVEP BCI paradigm. All users trained on these aforementioned systems also required a visual-based training protocol.…”

“… The overview of the state of the art in BCI and robotics as of 2023. Selected papers are featured, representative of the directions of the research, including ( A ) [ 29 ] and ( F ) [ 8 ]—the implants used to control limbs and exoskeleton using MI BCI paradigm; ( E ) [ 30 ]—the most recent paper to date, featuring a hybrid MI + SSVEP + EMG system; ( B ) [ 16 ], ( D ) [ 31 ], and ( G ) [ 32 ], all using the same cap for different robotic use cases as well as the MI BCI paradigm; ( C ) [ 33 ] featuring the P300 interface; and finally, ( H ) [ 34 ], the only paper to the best of our knowledge featuring a quadruped robot and using the SSVEP BCI paradigm. All users trained on these aforementioned systems also required a visual-based training protocol.…”