Automatic Removal of Multiple Artifacts for Single-Channel EEG

Removing different types of artifact 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 artifact, leading to a complex system if an EEG recording contains different types of artifact. With the advancement in wearable technologies, it is necessary to develop an energy efficient algorithm to deal with different types of artifact for single-channel wearable EEG devices. In this paper, an automatic EEG artifact removal algorithm is proposed that effectively reduces 3 types of artifact, i.e., ocular blink 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, TA/HA from a single EEG channel record as well as physical movement artifact.