Hu Hai, Guo Shengxin, Liu Ran, Wang Peng
State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China.
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
PeerJ. 2017 Jun 28;5:e3474. doi: 10.7717/peerj.3474. eCollection 2017.
Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).
从脑电图（EEG）信号中去除伪迹并提取节律对于便携式和可穿戴式EEG记录设备至关重要。我们提出了一种基于新颖分组规则的自适应奇异谱分析（SSA）方法，用于去除伪迹和提取节律。该分组规则根据EEG信号幅度自适应地确定前一个或两个SSA重构分量为伪迹并将其去除。然后，根据其余重构分量在傅里叶变换中的峰值频率进行分组，以提取所需的节律。因此，该分组规则使SSA能够适应包含不同伪迹和节律水平的EEG信号。基于马尔可夫过程幅度（MPA）EEG模型的模拟EEG数据以及睁眼和闭眼状态下的实验EEG数据用于验证自适应SSA方法。结果表明，与小波分解（WDec）和另外两种最近报道的SSA方法相比，该方法在去除伪迹和提取节律方面具有更好的性能。计算了使用自适应SSA提取的α节律的特征，以区分睁眼和闭眼状态。结果表明，其准确率（95.8%）高于WDec方法（79.2%）和无限脉冲响应（IIR）滤波方法（83.3%）。
