Robust Communication with IoT Devices using Wearable Brain Machine Interfaces

Proliferation of internet-of-things (IoT) will lead to scenarios where humans will interact with and control a variety of networked devices including sensors and actuators. Wearable brain-machine interfaces (BMI) can be a key enabler of this interaction for people with disabilities and limited motor skills. At the same time, BMI can improve the experience of healthy individuals significantly. However, state-of-the-art BMI systems have limited applicability as they are prone to errors even with sophisticated machine learning algorithms used for classifying the electroencephalogram (EEG) signals. We improve the reliability of BMI communication significantly by proposing two techniques at higher abstraction layers. Our first contribution is a command confirmation protocol that protects the brain-machine communication against false interpretations at run time. The second contribution is an off-line optimal event selection algorithm that identifies the most reliable subset of events supported by the target BMI system. The event selection is guided by novel user specific reliability metrics defined for the first time in this paper. Extensive experiments using a commercial BMI system demonstrate that the proposed techniques increase the communication robustness significantly, and reduce the time to complete a complex navigation task by 63% on average.