  Electroencephalography (EEG) recordings are analyzed using battery-powered
wearable devices to monitor brain activities and neurological disorders. These
applications require long and continuous processing to generate feasible
results. However, wearable devices are constrained with limited energy and
computation resources, owing to their small sizes for practical use cases.
Embedded heterogeneous multi-core platforms (HMPs) can provide better
performance within limited energy budgets for EEG applications. Error
resilience of the EEG application pipeline can be exploited further to maximize
the performance and energy gains with HMPs. However, disciplined tuning of
approximation on embedded HMPs requires a thorough exploration of the
accuracy-performance-power trade-off space. In this work, we characterize the
error resilience of three EEG applications, including Epileptic Seizure
Detection, Sleep Stage Classification, and Stress Detection on the real-world
embedded HMP test-bed of the Odroid XU3 platform. We present a combinatorial
evaluation of power-performance-accuracy trade-offs of EEG applications at
different approximation, power, and performance levels to provide insights into
the disciplined tuning of approximation in EEG applications on embedded
platforms.
