Berita BPI LIPI
oleh : Dr. Arjon Turnip , Iwan R. Setiawan
Analysis of EEG activity usually raises the problem of differentiating between genuine EEG activity and that which is introduced through a variety of external influence. These artifacts may affect the outcome of the EEG recording. In this paper, the Nonlinear Autoregressive (NAR) algorithm for artifacts removal of EEG signals in connection with the choice of the model structure (order) and computation of the system coefficients is proposed. The proposed method was tested in real EEG records acquired from eight subjects. The experimental result show that the proposed method can effectively remove the artifacts from all subjects.
Index Terms — Artifacts, nonliniear adaptive autoregressive, EEG.