A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA

International Journal of Biometric and Bioinformatics

Janett Walters-Williams¹  Yan Li¹ 

关键词: Independent Component Analysis;    Wavelet Transform;    Electroencephalogram (EEG);    Unscented Kalman Filter;    Cycle Spinning;   

DOI  :  

学科分类：计算机科学（综合）

来源: Computer Science Journals

【 摘 要 】

In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform.The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.

【 授权许可】

Unknown