这项研究的重点是开发基于运动图像(MI)的脑机接口(BMI),使用深度学习算法来控制下肢机器人外骨骼。该研究旨在通过利用深度学习的优势来克服传统BMI方法的局限性,如自动特征提取和迁移学习。评估BMI的实验方案设计为异步,允许受试者按照自己的意愿执行心理任务。
共有5名身体健康的受试者参加了一系列实验。来自其中两个会话的大脑信号用于通过迁移学习开发通用的深度学习模型。随后,在剩余的课程中对该模型进行了微调,并进行了评估.比较了三种不同的深度学习方法:一种没有经过微调,另一个微调了模型的所有层,第三个只微调了最后三层。评估阶段涉及参与者使用第二种深度学习方法进行解码的神经活动对外骨骼设备的专有闭环控制。
与基于每个受试者和实验阶段训练的空间特征的方法相比,对三种深度学习方法进行了评估。展示他们的卓越表现。有趣的是,没有微调的深度学习方法实现了与基于特征的方法相当的性能,这表明,在来自不同个体和以前会话的数据上训练的通用模型可以产生类似的效果。在三种深度学习方法中,进行了比较,微调所有层权重展示了最高的性能。
这项研究代表了迈向未来免校准方法的第一步。尽管努力通过利用其他受试者的数据来减少校准时间,完全消除被证明是不可能实现的。这项研究的发现对推进无校准方法具有显著意义,承诺将培训试验的需求降至最低。此外,本研究中采用的实验评估方案旨在复制现实生活场景,在行走或停止步态等行为的决策中,给予参与者更高的自主权。
This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The
study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.
A total of five healthy able-bodied subjects were enrolled in this
study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants\' neural activity using the second deep learning approach for the decoding.
The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.
This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The
study\'s discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this
study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.