Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria.
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands.
J Neural Eng. 2022 May 9;19(3). doi: 10.1088/1741-2552/ac689f.
. In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement.. Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation-only condition, and once while simultaneously attempting movement.. We observed mean correlations well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. Additionally, no global improvement over three sessions within five days, both in sensor and in source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found.. No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.
在患有颈椎脊髓损伤 (SCI) 或退行性疾病导致运动功能受限的人群中，恢复上肢运动一直是脑机接口领域的目标。最近，我们的研究小组研究了使用非侵入性和实时的方法，从低频脑信号中解码健康参与者在目标跟踪任务中的连续运动。为了将我们的设置推进到运动障碍的终端用户，我们选择了一种新的基于尝试运动的范式。在此，我们呈现了两项研究的结果。在第一项研究中，我们研究了十位健康参与者在屏幕上完成目标跟踪/形状跟踪任务时的数据，以评估由于用户训练而导致的解码性能的提高。在第二项研究中，一位脊髓损伤患者进行了相同的任务。为了研究在脊髓损伤患者中使用尝试运动的优点，我们记录了脊髓损伤患者的数据，分别在观察条件下和同时尝试运动的条件下进行记录。我们观察到，在健康参与者中，基于尝试运动的连续运动解码的平均相关性明显高于随机水平。此外，在五天内的三个会话中，在所有参与者和运动参数中，都没有观察到传感器和源空间的全局改善。在脊髓损伤患者中，解码性能明显高于随机水平。在健康参与者中，没有观察到连续尝试运动解码中的学习效应。相比之下，在源空间中，非显著变化的解码模式可能会促进使用源空间解码，因为利用迁移学习的广义解码器。此外，在一位脊髓损伤患者中，尝试运动解码的相关性高于仅观察和执行运动的相关性，这表明尝试运动解码可能是健康参与者的可行性研究与运动障碍终端用户的实际应用之间的一个可能的联系。
