用脑电图同时解码抓握动作的运动与力学参数
Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging
- 用注意力机制回归模型从脑电信号中解码多参数
- 最高准确率R²达0.8，延迟仅29.2毫秒
- 适合开发实时多指令脑机接口系统
脑机接口（BMIs）可帮助行动受限者如中风患者或截肢者。当前关键挑战在于提升可用性与控制精度，可通过准确解码多个运动学和动力学参数实现。本文提出三种回归模型：偏最小二乘回归器、多层感知机和基于注意力的回归器，用于从脑电信号中解码多种运动参数。在WAY EEG GAL数据集上评估，对比了个体特定与跨个体条件下两种策略：单一模型统一解码所有参数，以及为每个参数分别建模的基线方法。结果表明，基于注意力的回归器表现最佳，达到R²=0.8，延迟29.2毫秒，显著提升多参数同时解码性能；但单参数解码时表现下降。多层感知机则表现更稳定但准确率较低（R²=0.49）。研究验证了注意力模型在实时多命令脑机接口中的潜力，推动更自然控制设备的发展。
原文摘要 · Abstract (English)
Brain-machine interfaces (BMIs) can assist individuals with limited mobility, such as stroke survivors or amputees. One of the key challenges in developing BMIs is expanding their usability and control, which can be achieved by accurately decoding multiple kinematic and kinetic parameters. To address this, we propose three regression models: partial least squares regressor, multilayered perceptron, and attention based regressor, to decode multiple movement parameters from EEG signals. We evaluated these models on the WAY EEG GAL dataset, focusing on their performance under subject specific and subject independent conditions with two strategies: a single model for all parameters and a baseline with separate models for each parameter. Among all regressors, the attention based regressor achieved the best performance, with an $R^2$ of 0.8 and a latency of 29.2 milliseconds, demonstrating significant improvement in simultaneous multi parameter decoding. However, its performance dropped for single parameter decoding. The multi layered perceptron showed more consistent but lower accuracy across both decoding types ($R^2$ = 0.49). These findings highlight the potential of attention based models for real time multi command BMI systems and contribute to the development of more intuitive control devices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。