Removing different types of artifacts from the electroencephalography (EEG) recordings is a critical
step in performing EEG signal analysis and diagnosis. Most of the existing algorithms aim for removing single
type of artifacts, leading to a complex system if an EEG recording contains different types of artifacts. With the
advancement in wearable technologies, it is necessary to develop an energy-efficient algorithm to deal with different
types of artifacts for single-channel wearable EEG devices. In this paper, an automatic EEG artifact removal
algorithm is proposed that effectively reduces three types of artifacts, i.e., ocular artifact (OA), transmission-
line/harmonic-wave artifact (TA/HA), and muscle artifact (MA), from a single-channel EEG recording. The
effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-
MIT dataset. The experimental results show that the proposed algorithm effectively suppresses OA, MA and
TA/HA from a single-channel EEG recording as well as physical movement artifact.
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