Adapting Artifact Subspace Reconstruction Method for SingleChannel EEG using Signal Decomposition Techniques.
Annu Int Conf IEEE Eng Med Biol Soc
; 2023: 1-4, 2023 07.
Article
em En
| MEDLINE
| ID: mdl-38083141
ABSTRACT
Artifact removal from electroencephalography (EEG) data is a crucial step in the analysis of neural signals. One method that has been gaining popularity in recent years is Artifact Subspace Reconstruction (ASR), which is highly effective in eliminating large amplitude and transient artifacts in EEG data. However, traditional ASR is not possible to use with single-channel EEG data. In this study, we propose incorporating signal decomposition techniques such as ensemble empirical mode decomposition (EEMD), wavelet transform (WT), and singular spectrum analysis (SSA) into ASR, to decompose single-channel data into multiple components. This allows the ASR process to be applied effectively to the data. Our results show that the proposed single-channel version of ASR outperforms its counterpart Independent Component Analysis (ICA) methods when tested on two open datasets. Our findings indicate that ASR has significant potential as a powerful tool for removing artifacts from EEG data analysis.Clinical Relevance- This provided artifact removal technique for single-channel EEG.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Artefatos
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article