# BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2509.03521 · HTML: https://mlsanity.com/paper/2509.03521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017)
- [Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency](https://mlsanity.com/paper/1512.08301/) (2015)
- [NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC](https://mlsanity.com/paper/2205.06090/) (2022)
- [Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs](https://mlsanity.com/paper/2204.02362/) (2022)
- [Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology](https://mlsanity.com/paper/2405.10780/) (2024)

## Abstract (arXiv)

Decoding bimanual hand movements from intracortical recordings remains a critical challenge for brain-computer interfaces (BCIs), due to overlapping neural representations and nonlinear interlimb interactions. We introduce BiND (Bimanual Neural Discriminator-Decoder), a two-stage model that first classifies motion type (unimanual left, unimanual right, or bimanual) and then uses specialized GRU-based decoders, augmented with a trial-relative time index, to predict continuous 2D hand velocities. We benchmark BiND against six state-of-the-art models (SVR, XGBoost, FNN, CNN, Transformer, GRU) on a publicly available 13-session intracortical dataset from a tetraplegic patient. BiND achieves a mean R2 of 0.76 (±0.01) for unimanual and 0.69 (±0.03) for bimanual trajectory prediction, surpassing the next-best model (GRU) by 2% in both tasks. It also demonstrates greater robustness to session variability than all other benchmarked models, with accuracy improvements of up to 4% compared to GRU in cross-session analyses. This highlights the effectiveness of task-aware discrimination and temporal modeling in enhancing bimanual decoding.