# ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

arXiv: https://arxiv.org/abs/2006.07900 · HTML: https://mlsanity.com/paper/2006.07900/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 12 of 12)

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- [A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces](https://mlsanity.com/paper/2304.11106/) (2023)
- [Hardware-Efficient EMG Decoding for Next-Generation Hand Prostheses](https://mlsanity.com/paper/2405.20052/) (2024)
- [ODTE -- An ensemble of multi-class SVM-based oblique decision trees](https://mlsanity.com/paper/2411.13376/) (2024)
- [MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection](https://mlsanity.com/paper/2503.08251/) (2025)
- [Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics](https://mlsanity.com/paper/2505.02516/) (2025)
- [BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation](https://mlsanity.com/paper/2601.17625/) (2026)

## Abstract (arXiv)

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.