# Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2408.03336 · HTML: https://mlsanity.com/paper/2408.03336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [Nengo and low-power AI hardware for robust, embedded neurorobotics](https://mlsanity.com/paper/2007.10227/) (2020)
- [Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram](https://mlsanity.com/paper/2208.06900/) (2022)

## Built on by (most cited first, 3 of 3)

- [EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover](https://mlsanity.com/paper/2602.20041/) (2026)
- [Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving](https://mlsanity.com/paper/2604.19368/) (2026)
- [NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware](https://mlsanity.com/paper/2609.24391/) (2026)

## Abstract (arXiv)

Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a 1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.