Kim Hyeonseok, Chang Chi-Yuan, Kothe Christian, Iversen John Rehner, Miyakoshi Makoto
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, United States; Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States.
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, United States.
J Neurosci Methods. 2025 Aug;420:110465. doi: 10.1016/j.jneumeth.2025.110465. Epub 2025 May 3.
To improve the Artifact Subspace Reconstruction (ASR) algorithm's performance for real-world EEG data by addressing the problem of low-quality or no calibration data identification in the original ASR (ASR) algorithm.
We proposed a new method for defining high-quality calibration data using point-by-point amplitude evaluation to eliminate collateral rejection of clean data, which is identified as the major cause of the problem with ASR. We compared non-parametric and parametric approaches, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Generalized Extreme Value (GEV) distribution (ASR and ASR, respectively).
RESULTS (COMPARISON WITH EXISTING METHODS): We demonstrated the effectiveness of these approaches on simulated and real EEG data. Simulation results showed that ASR and ASR removed simulated artifacts completely where ASR failed, both in time- and frequency-domain evaluations. In empirical data from 205-channel EEG recordings during a three-ball juggling task (n = 13), ASR found 42 % and ASR found 24 % of data usable for calibration on average, compared to only 9 % by ASR. Subsequent Independent Component Analysis (ICA) showed that data preprocessed with ASR and ASR produced brain ICs that accounted for more variance of the original data (30 % and 29 %) compared to ASR (26 %).
The proposed ASR and ASR methods handle motion-related artifacts better than the original ASR algorithm, enabling researchers to better extract brain activity during real-world motor tasks. These methods provide a practical advantage in processing EEG data from experiments involving high-intensity motor activities, advancing biomedical research capabilities.
通过解决原始伪迹子空间重建（ASR）算法中低质量或无校准数据识别的问题，提高ASR算法对实际脑电图（EEG）数据的性能。
我们提出了一种使用逐点幅度评估来定义高质量校准数据的新方法，以消除对干净数据的附带拒绝，这被确定为ASR问题的主要原因。我们比较了非参数和参数方法，即基于密度的带噪声应用空间聚类（DBSCAN）和广义极值（GEV）分布（分别为ASR和ASR）。
结果（与现有方法比较）：我们在模拟和实际EEG数据上证明了这些方法的有效性。模拟结果表明，在时域和频域评估中，当ASR失败时，ASR和ASR能完全去除模拟伪迹。在一项三球杂耍任务（n = 13）期间的205通道EEG记录的实证数据中，与ASR仅9%的数据可用于校准相比，ASR平均发现42%的数据可用于校准，ASR发现24%的数据可用于校准。随后的独立成分分析（ICA）表明，与ASR（26%）相比，用ASR和ASR预处理的数据产生的脑独立成分（IC）占原始数据的方差更大（30%和29%）。
所提出的ASR和ASR方法在处理与运动相关的伪迹方面比原始ASR算法更好，使研究人员能够在实际运动任务中更好地提取脑活动。这些方法在处理来自涉及高强度运动活动的实验的EEG数据方面具有实际优势，提升了生物医学研究能力。
