Dekleva Brian M, Collinger Jennifer L
Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, United States of America.
Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America.
J Neural Eng. 2025 Feb 11;22(1):016036. doi: 10.1088/1741-2552/adaa1f.
Real-world implementation of brain-computer interfaces (BCIs) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control. However, the relation between cortical activity and behavior is not stationary: neural responses that appear related to a certain aspect of behavior (e.g. grasp force) in one context will exhibit a relationship to something else in another context (e.g. reach speed). This presents a challenge for generalizable decoding, since the applicability of a decoder for a given parameter changes over time.We developed a method to simplify the problem of continuous decoding that uses transient, end effector-specific neural responses to identify periods of relevant effector engagement. Specifically, we use transient responses in the population response observed at the onset and offset of all hand-related actions to signal the applicability of hand-related feature decoders (e.g. digit movement or force). By using this transient-based gating approach, specific feature decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions.The transient-based decoding approach enabled high-quality online decoding of grasp force and individual finger control in multiple behavioral paradigms. The benefits of the gated approach are most evident in tasks that require both hand and arm control, for which standard continuous decoding approaches exhibit high output variability.The approach proposed here addresses the challenge of decoder generalization across contexts. By limiting decoding to identified periods of effector engagement, this approach can support reliable BCI control in real-world applications.Clinical Trial ID: NCT01894802.
用于设备连续控制的脑机接口（BCI）在现实世界中的应用理想情况下应依赖于完全异步解码方法。也就是说，解码算法应通过从实时神经活动中估计用户的预期动作来持续更新其输出，而无需与外部提示进行任何时间对齐。这种开放式的时间灵活性对于实现自然直观的控制是必要的。然而，皮层活动与行为之间的关系并非固定不变：在一种情境中看似与行为的某个特定方面（如抓握力）相关的神经反应，在另一种情境中（如伸展速度）会与其他事物表现出相关性。这给通用解码带来了挑战，因为针对给定参数的解码器的适用性会随时间变化。我们开发了一种方法来简化连续解码问题，该方法利用短暂的、特定于末端执行器的神经反应来识别相关效应器参与的时期。具体而言，我们利用在所有与手相关动作的开始和结束时观察到的群体反应中的短暂反应，来表明与手相关的特征解码器（如手指运动或力量）的适用性。通过使用这种基于短暂反应的门控方法，特定的特征解码模型可以更简单（由于局部线性），并且对诸如伸手和抓握动作组合等跨效应器干扰的干扰不太敏感。基于短暂反应的解码方法在多种行为范式中实现了对抓握力和单个手指控制的高质量在线解码。门控方法的优势在需要手和手臂控制的任务中最为明显，对于这些任务，标准的连续解码方法表现出高输出变异性。这里提出的方法解决了跨情境解码器泛化的挑战。通过将解码限制在已识别的效应器参与时期，这种方法可以支持现实世界应用中的可靠BCI控制。临床试验标识：NCT01894802。
