Combining new classes of nanomembrane electrodes with flexible electronics and a deep learning algorithm could help disabled people wirelessly control an electric wheelchair, interact with a computer, or operate a small robotic vehicle without donning a bulky hair-electrode cap or contending with wires.
The project was conducted by researchers from the Georgia Institute of Technology, University of Kent, and Wichita State University. “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,” said Woon-Hong Yeo, an assistant professor in Georgia Tech’s George W. Woodruff School of Mechanical Engineering and Wallace H. Coulter Department of Biomedical Engineering. “The primary innovation is in the development of a fully integrated package of high-resolution EEG monitoring systems and circuits within a miniaturized skin-conformal system.”
The Brain-Machine Interface (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) now requires use of an electrode-studded hair cap that uses wet electrodes, adhesives and wires to connect with computer equipment that interprets the signals.
Yeo and his collaborators are taking advantage of a new class of flexible, wireless sensors and electronics that can be easily 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; and soft, flexible circuity with a Bluetooth telemetry unit. The recorded EEG data from the brain is processed in the circuitry, then wirelessly delivered to a tablet computer via Bluetooth up to 15 meters away.
Beyond the sensing requirements, detecting and analyzing SSVEP signals has been challenging because of the low signal amplitude, in the range of tens of microvolts, which is similar to electrical noise in the body. Researchers must also deal with variation in human brains. Yet accurately measuring the signals is essential to determining what the user wants the system to do.
To address those challenges, the research team turned to deep learning neural network algorithms. They say that, like pictures of a dog, which can have a lot of variations, EEG signals have the same challenge of high variability. Deep learning methods have been proven to work well with pictures and it has been shown that they can work very well with EEG signals as well.
In addition, the researchers used deep learning models to identify which electrodes are the most useful for gathering information to classify EEG signals. They found that the model is able to identify the relevant locations in the brain for BMI, in agreement with human experts. This reduces the number of sensors needed, cutting cost and improving portability.
The system uses three elastomeric scalp electrodes held onto the head with a fabric band, ultrathin wireless electronics conformed to the neck, and a skin-like printed electrode placed on the skin below an ear. The dry soft electrodes adhere to the skin and do not use adhesive or gel. Along with ease of use, the system could reduce noise and interference and provide higher data transmission rates compared to existing systems.
The system was evaluated with six human subjects. The results showed that the deep learning algorithm with real-time data classification could control an electric wheelchair and a small robotic vehicle. The signals could also be used to control a display system without using a keyboard, joystick, or other controller. Currently, EEG systems must cover the majority of the scalp to get signals, so potential users may be sensitive about wearing them. This miniaturized, wearable soft device is designed to be comfortable for long-term use.
Next steps will include improving the electrodes and making the system more useful for motor- impaired individuals. Future study will focus on investigation of fully elastomeric, wireless self-adhesive electrodes that can be 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.
This EEG monitoring system also has the potential to allow scientists to monitor human neural activity in a relatively unobtrusive way as subjects go about their lives. For example, it is currently being used in a similar system to monitor neural activity while people sleep in the comfort of their own homes, rather than in the lab with bulky, rigid, uncomfortable equipment, as is customarily done. Measuring sleep-related neural activity with an imperceptible system may help identify new, non-invasive biomarkers of Alzheimer's-related neural pathology, which is predictive of dementia.