  Currently, people with disability or difficulty to move their arms (referred
to as "patients") have very limited technological solutions to efficiently
address their physiological limitations. It is mainly due to two reasons: (1)
the non-invasive solutions like mind-controlled prosthetic devices are
typically very costly and require expensive maintenance; and (2) other
solutions require costly invasive brain surgery, which is high risk to perform,
expensive, and difficult to maintain. Therefore, current technological
solutions are not accessible for all patients with different financial
backgrounds. Toward this, we propose a low-cost technological solution called
MindArm, a mechanized intelligent non-invasive neuro-driven prosthetic arm
system. Our MindArm system employs a deep neural network (DNN) engine to
translate brain signals into the intended prosthetic arm motion, thereby
helping patients to perform many activities despite their physiological
limitations. Here, our MindArm system utilizes widely accessible and low-cost
surface electroencephalogram (EEG) electrodes coupled with an Open Brain
Computer Interface and UDP networking for acquiring brain signals and
transmitting them to the compute module for signal processing. In the compute
module, we run a trained DNN model to interpret normalized micro-voltage of the
brain signals, and then translate them into a prosthetic arm action via serial
communication seamlessly. The experimental results on a fully working prototype
demonstrate that, from the three defined actions, our MindArm system achieves
positive success rates, i.e., 91\% for idle/stationary, 85\% for shake hand,
and 84\% for pick-up cup. This demonstrates that our MindArm provides a novel
approach for an alternate low-cost mind-controlled prosthetic devices for all
patients.
