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. By providing a fully portable,
wireless brain-machine interface (BMI), the wearable system could offer an
improvement over conventional electroencephalography (EEG) for measuring
signals from visually evoked potentials in the human brain. The system's
ability to measure EEG signals for BMI has been evaluated with six human
subjects, but has not been studied with disabled individuals. The project was conducted
by researchers from the Georgia Institute of Technology, University of Kent and
Wichita State University. 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.
Researchers 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, and 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. The system was evaluated with six human subjects.
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