# Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces

arXiv: https://arxiv.org/abs/2004.11690 · HTML: https://mlsanity.com/paper/2004.11690/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets](https://mlsanity.com/paper/2311.16109/) (2023)
- [vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders](https://mlsanity.com/paper/2312.09449/) (2023)
- [Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces](https://mlsanity.com/paper/2102.11221/) (2021)
- [On using AI for EEG-based BCI applications: problems, current challenges and future trends](https://mlsanity.com/paper/2506.16168/) (2025)
- [Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2601.07556/) (2026)
- [AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders](https://mlsanity.com/paper/2609.30037/) (2026)
- [EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review](https://mlsanity.com/paper/2609.32930/) (2026)

## Abstract (arXiv)

Motor-Imagery Brain--Machine Interfaces (MI-BMIs)promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG). Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery-operated device with low average power consumption for long-term use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural Network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr.Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64x and a reduction of up to 85% in memory footprint with respect to a single-core layer-wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0GMAC/s/W, it is 256x more energy-efficient than an EEGNET implementation on an ARM Cortex-M7 (0.082GMAC/s/W).