Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genoa, Italy; Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA; Shirley Ryan Ability Lab, Chicago, IL, 60611, USA.
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genoa, Italy.
Neural Netw. 2021 May;137:174-187. doi: 10.1016/j.neunet.2021.01.009. Epub 2021 Jan 20.
In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body-machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users' task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process.
在人机接口中，解码器校准对于实现与机器的有效和无缝交互至关重要。然而，由于闭环动态和用户适应，解码器离线预测能力通常并不意味着易用性，因此通常需要重新校准。在这里，我们提出了一种自适应接口，该接口利用经过迭代训练的非线性自动编码器在线执行流形识别和跟踪，具有减少接口重新校准的必要性和提高人机联合性能的双重目标。重要的是，所提出的方法避免了中断设备的操作，它既不依赖于任务状态的信息，也不依赖于稳定的神经或运动流形的存在，允许它在接口操作的早期阶段应用，此时新的神经策略的形成仍在进行中。为了更直接地测试我们算法的性能，我们将自动编码器的潜在空间定义为身体-机器接口的控制空间。在初始的离线参数调整之后，我们评估了自适应接口相对于静态解码器的性能，以模拟同时在潜在空间内学习执行到达运动的用户不断变化的低维流形。结果表明，自适应方法提高了接口解码器的表示效率。同时，它显著提高了用户的任务相关性能，这表明在线共同适应过程鼓励了更准确的内部模型的发展。
