  Classifiers that can be implemented on chip with minimal computational and
memory resources are essential for edge computing in emerging applications such
as medical and IoT devices. This paper introduces a machine learning model
based on oblique decision trees to enable resource-efficient classification on
a neural implant. By integrating model compression with probabilistic routing
and implementing cost-aware learning, our proposed model could significantly
reduce the memory and hardware cost compared to state-of-the-art models, while
maintaining the classification accuracy. We trained the resource-efficient
oblique tree with power-efficient regularization (ResOT-PE) on three neural
classification tasks to evaluate the performance, memory, and hardware
requirements. On seizure detection task, we were able to reduce the model size
by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of
boosted trees, using the intracranial EEG from 10 epilepsy patients. In a
second experiment, we tested the ResOT-PE model on tremor detection for
Parkinson's disease, using the local field potentials from 12 patients
implanted with a deep-brain stimulation (DBS) device. We achieved a comparable
classification performance as the state-of-the-art boosted tree ensemble, while
reducing the model size and feature extraction cost by 10.6X and 6.8X,
respectively. We also tested on a 6-class finger movement detection task using
ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature
computation cost by 5.1X. The proposed model can enable a low-power and
memory-efficient implementation of classifiers for real-time neurological
disease detection and motor decoding.
