Wearable Brain Interface combines new classes of nanomembrane electrodes with flexible electronics and a deep learning algorithm. Potentially, helping disabled people, wirelessly control an electric wheelchair. Interacting with a computer or operate a small robotic vehicle without donning a bulky hair-electrode cap or contending with wires.
By providing a fully portable, wireless brain-machine interface (BMI), the wearable system offers an improvement over conventional electroencephalography (EEG). Measuring signals from visually evoked potentials in the human brain. The system’s ability to measure EEG signals for BMI used six human subjects . As of yet, disabled individuals used later in the program.
Ergonomic EEG System
Wearable Brain-Machine Interface, this work reports fundamental strategies to design an ergonomic, portable EEG system for a broad range of assistive devices, smart home systems and neuro-gaming interfaces. The primary innovation is in the development of a fully integrated package of high-resolution EEG monitoring systems. Along with, circuits within a miniaturized skin-conformal system. https://glewengineering.com/light-sensors-could-improve-medical-devices-and-security-imaging/
In addition, BMI is an essential part of rehabilitation technology that allows those with amyotrophic lateral sclerosis (ALS), chronic stroke, or other severe motor disabilities to control prosthetic systems. Gathering brain signals known as steady-state virtually evoked potentials (SSVEP) . Requiring use of an electrode-studded hair cap that uses wet electrodes, adhesives and wires to connect with computer equipment that interprets the signals.
In addition, collaborators will take advantage of a new class of flexible, wireless sensors and electronics applied to the skin. The system includes three primary components. Highly flexible, hair-mounted electrodes that make direct contact with the scalp through hair. an ultrathin nanomembrane electrode. Soft, flexible circuity with a Bluetooth telemetry unit. The recorded EEG data from the brain is processed in the flexible circuitry, then wirelessly delivered to a tablet computer via Bluetooth from up to 15 m away.
Controlling Wheelchairs – Sensing Requirements
Beyond the sensing requirements, detecting and analyzing SSVEP signals challenge the team, because of the low signal amplitude. Which is in the range of tens of microvolts, similar to electrical noise in the body. Researchers also deal with variation in human brains. Also, accurately measuring the signals is essential to determining what the user wants the system to activate. To address those challenges, the research team turned to deep learning neural network algorithms running on the flexible circuitry.
Wearable Brain-Machine Interface with deep learning methods, commonly used to classify pictures of everyday things such as cats and dogs, to analyze the EEG signals. Like pictures of a dog contain ot of variations. EEG signals contain the same challenge of high variability. Deep learning methods proven to work well with pictures, and we show that they work very well with EEG signals.
In addition, the researchers used deep learning models to identify which electrodes most useful for gathering information to classify EEG signals. It was found that the model is able to identify the relevant locations in the brain for BMI. Which is in agreement with human experts. This reduces the number of sensors we need, cutting cost and improving portability.https://glewengineering.com/researchers-developed-tiny-lensless-endoscope/
Wearable Brain Interface Worn on Scalp
This Wearable Brain Interface system uses three elastomeric scalp electrodes held onto the head with a fabric band. Ultrathin wireless electronics conformed to the neck. Also, a skin-like printed electrode placed on the skin below an ear. The dry soft electrodes adhere to the skin and with no adhesive or gel. Along with ease of use, the system reduces noise and interference. Provide higher data transmission rates compared to existing systems.
For that reason, the system evaluated six human subjects. The deep learning algorithm with real-time data classification controls an electric wheelchair and a small robotic vehicle. The signals used to control a display system without using a keyboard, joystick, or other controller.
Typical EEG systems covers the majority of the scalp to recover signals. This miniaturized, wearable soft device is fully integrated. Designed with comfort for long-term use. Next steps will include improving the electrodes and making the system more useful for motor-impaired individuals. https://glewengineering.com/brain-tumor-imaging-protein-in-scorpion-venom/
Also, this Wearable Brain Interface future study focus is on investigation of fully elastomeric (Coatings) . Wireless self-adhesive electrodes mounted on the hairy scalp without any support from headgear. Along with further miniaturization of the electronics to incorporate more electrodes for use with other studies. The EEG system is reconfigured to monitor motor-evoked potentials or motor imagination for motor-impaired subjects. Which further studied as a future work on therapeutic applications. Therefore, long term, the system is potentially for other applications where simpler EEG monitoring dsiplay helfulness. https://www.emotiv.com/brain-controlled-technology/
Inconclusion: Wearable Brain Interface
In conclusion, this Wearable Brain Interface, EEG monitoring system has the potential to finally allow scientists to monitor human neural activity in a relatively unobtrusive way as subjects go about their lives. For example, engineers currently using a similar system to monitor neural activity while people sleep in the comfort of their own homes, rather than the lab . Therefore, measuring sleep-related neural activity with an imperceptible system may allow us to identify new, noninvasive biomarkers of Alzheimer’s-related neural pathology predictive of dementia.