The Influence Assessment of Artifact Subspace Reconstruction on the EEG Signal Characteristics

EEG signals may be affected by physiological and non-physiological artifacts hindering the analysis of brain activity. Blind source separation methods such as independent component (ICA) are effective ways improving signal quality removing components representing non-brain However, most ICA-based artifact removal strategies have limitations, individual differences in visual assessment components. These limitations might reduced introducing automatic selection for ICA On other hand, new fully developed. One method is subspace reconstruction (ASR). ASR a component-based approach, which can used automatically with small calculation requirements. The was originally designed to run not instead of, but addition ICA. We compared two correction approaches: approach based only on where applied additionally before case study data collected from 10 subjects performing four popular experimental paradigms, including resting-state, stimulation oddball task. Statistical signal-to-noise ratio showed significant difference, between followed results show that both provided similar quality, they were characterised different usabilities.