Janir Ramos da Cruz1, Vitaly Chicherov2, Michael H Herzog2, Patrícia Figueiredo3. 1. Institute for Systems and Robotics - Lisbon (LARSys) and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugal; Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Switzerland. Electronic address: janir.ramos@epfl.ch. 2. Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Switzerland. 3. Institute for Systems and Robotics - Lisbon (LARSys) and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugal.
Abstract
OBJECTIVE: With the advent of high-density EEG and studies of large numbers of participants, yielding increasingly greater amounts of data, supervised methods for artifact rejection have become excessively time consuming. Here, we propose a novel automatic pipeline (APP) for pre-processing and artifact rejection of EEG data, which innovates relative to existing methods by not only following state-of-the-art guidelines but also further employing robust statistics. METHODS: APP was tested on event-related potential (ERP) data from healthy participants and schizophrenia patients, and resting-state (RS) data from healthy participants. Its performance was compared with that of existing automatic methods (FASTER for ERP data, TAPEEG and Prep pipeline for RS data) and supervised pre-processing by experts. RESULTS: APP rejected fewer bad channels and bad epochs than the other methods. In the ERP study, it produced significantly higher amplitudes than FASTER, which were consistent with the supervised scheme. In the RS study, it produced spectral measures that correlated well with the automatic alternatives and the supervised scheme. CONCLUSION: APP effectively removed EEG artifacts, performing similarly to the supervised scheme and outperforming existing automatic alternatives. SIGNIFICANCE: The proposed automatic pipeline provides a reliable and efficient tool for pre-processing large datasets of both evoked and resting-state EEG.
OBJECTIVE: With the advent of high-density EEG and studies of large numbers of participants, yielding increasingly greater amounts of data, supervised methods for artifact rejection have become excessively time consuming. Here, we propose a novel automatic pipeline (APP) for pre-processing and artifact rejection of EEG data, which innovates relative to existing methods by not only following state-of-the-art guidelines but also further employing robust statistics. METHODS:APP was tested on event-related potential (ERP) data from healthy participants and schizophreniapatients, and resting-state (RS) data from healthy participants. Its performance was compared with that of existing automatic methods (FASTER for ERP data, TAPEEG and Prep pipeline for RS data) and supervised pre-processing by experts. RESULTS:APP rejected fewer bad channels and bad epochs than the other methods. In the ERP study, it produced significantly higher amplitudes than FASTER, which were consistent with the supervised scheme. In the RS study, it produced spectral measures that correlated well with the automatic alternatives and the supervised scheme. CONCLUSION:APP effectively removed EEG artifacts, performing similarly to the supervised scheme and outperforming existing automatic alternatives. SIGNIFICANCE: The proposed automatic pipeline provides a reliable and efficient tool for pre-processing large datasets of both evoked and resting-state EEG.
Authors: Janir da Cruz; João Rodrigues; John C Thoresen; Vitaly Chicherov; Patrícia Figueiredo; Michael H Herzog; Carmen Sandi Journal: Cereb Cortex Date: 2018-10-01 Impact factor: 5.357