State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
J Neural Eng. 2023 Nov 28;20(6). doi: 10.1088/1741-2552/ad01de.
Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.
从脑电图 (EEG) 中无创解码不同类型的运动是神经工程，特别是脑机接口中的一个重要课题。虽然广泛使用的感觉运动节律 (SMR) 在肢体解码中很有效，但它在解码运动频率方面效果不佳。越来越多的证据支持这样一种观点，即运动频率是在稳态运动相关节律 (SSMRR) 中编码的。我们的研究有两个主要目标：首先，研究在自愿运动期间 EEG 中 SSMRR 的空间-频谱表示；其次，评估是否可以基于 SSMRR 有效地解码运动频率和肢体。为了全面检查 SSMRR 的表示形式，我们研究了与各种节律性手指运动相关的 EEG 的频率特征和空间模式。对传感器或源域 EEG 与数据手套记录的手指运动之间进行相干性分析。基于频谱 SNR 特征和滤波器组公共空间模式特征的融合模型用于解码运动频率和肢体。在组级、传感器域和源域相干图中，证明了准确的运动频率 (f0) 和其第一谐波 (f1) 是在对侧运动皮层中编码的。对于包括双手两个运动频率的四分类，外部节拍和内部节拍运动的解码准确率分别为 73.14±15.86%和 66.30±17.26%（十个被试的平均值，机会水平为 31.05%和 30.96%）。值得注意的是，五个解码准确率最高的被试的平均结果达到 87.21±7.44%和 80.44±7.99%。我们的结果验证了 SSMRR 的 EEG 表示，并证明可以基于从 SSMRR 中提取的空间-频谱特征有效地解码运动频率和肢体。我们建议 SSMRR 可以作为 SMR 的补充，以扩展可解码运动类型的范围和肢体解码的方法。
