Artifacts removal in EEG signal using a new neural network enhanced adaptive filter
作者： Aysa Jafarifarmand , Mohammad Ali Badamchizadeh
DOI: 10.1016/J.NEUCOM.2012.09.024
关键词:
摘要: EEG signal is an important clinical tool for diagnosing, monitoring, and managing neurological disorders. This often affected by a variety of large contaminations or artifacts, which reduce its usefulness. In this paper, new adaptive FLN-RBFN-based filter proposed to cancel the three most serious contaminants, i.e. ocular, muscular cardiac artifacts from signal. The basic method used in paper elimination noise cancellation (ANC). results demonstrate effectiveness technique extracting desired component contaminated
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