通过无创脑成像在抓握和举起任务中同时解码动力学和运动学运动参数
Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging
浏览论文内容
中文总结 AI 辅助
研究旨在通过无创脑成像在抓握和举起任务中同时解码运动参数，提出偏最小二乘回归器、多层感知器和基于注意力的回归器三种模型，以脑电图信号解码，在特定数据集上评估，基于注意力的回归器在多参数解码表现最佳，为BMI系统发展助力。
中文摘要 AI 辅助
脑机接口（BMI）可帮助行动受限者，如中风幸存者或截肢者。开发BMI的关键挑战之一是扩展其可用性和控制能力，可通过准确解码多个运动学和动力学参数来实现。为此，我们提出三种回归模型：偏最小二乘回归器、多层感知器和基于注意力的回归器，用于从脑电图信号中解码多个运动参数。我们在WAY EEG GAL数据集上评估这些模型，重点关注它们在特定受试者和独立于受试者条件下的性能，采用两种策略：对所有参数使用单个模型和对每个参数使用单独模型的基线。在所有回归器中，基于注意力的回归器性能最佳，$R^2$为0.8，延迟为29.2毫秒，在同时多参数解码方面有显著改进。但其单参数解码性能下降。多层感知器在两种解码类型中表现更一致但准确性较低（$R^2$ = 0.49）。这些发现突出了基于注意力的模型在实时多指令BMI系统中的潜力，并有助于开发更直观的控制设备。
英文摘要
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.
发表机构
- IIT Gandhinagar（印度理工学院甘地纳格尔分校）
机构由 AI 辅助整理，请以论文原文为准。
