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 flexible circuitry, then wirelessly delivered to a tablet computer via Bluetooth from up to 15 meters away.
Beyond the sensing requirements, detecting and analyzing SSVEP signals have been challenging because of the low signal amplitude, which is in the range of tens of micro-volts, similar to electrical noise in the body. Researchers also must 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 running on the flexible circuitry.
In addition, the researchers used deep learning models to identify which electrodes are the most useful for gathering information to classify EEG signals. “We found that the model is able to identify the relevant locations in the brain for BMI, which is in agreement with human experts,” Ang added. “This reduces the number of sensors we need, 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 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, Yeo said.
“Typical EEG systems must cover the majority of the scalp to get signals, but potential users may be sensitive about wearing them,” Yeo added. “This miniaturized, wearable soft device is fully integrated and 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 would 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,” Yeo said. “The EEG system can also be reconfigured to monitor motor-evoked potentials or motor imagination for motor-impaired subjects, which will be further studied as a future work on therapeutic applications.”
Long-term, the system may have potential for other applications where simpler EEG monitoring would be helpful, such as in sleep studies done by Audrey Duarte, an associate professor in Georgia Tech’s School of Psychology.
“This 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,” she said. “For example, Dr. Yeo and I are currently using a similar system to monitor neural activity while people sleep in the comfort of their own homes, rather than the lab with bulky, rigid, uncomfortable equipment, as is customarily done. Measuring sleep-related neural activity with an imperceptible system may allow us to identify new, non-invasive biomarkers of Alzheimer’s-related neural pathology predictive of dementia.”
Source and top image: Georgia Institute of Technology