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Using principal components analysis to examine resting state EEG in relation to task performance

Journal Article


Abstract


  • Brain dynamics research has highlighted the significance of the ongoing EEG in ERP genesis and cognitive functioning. Few studies, however, have assessed the contributions of the intrinsic resting state EEG to these stimulus-response processes and behavioral outcomes. Principal components analysis (PCA) has increasingly been used to obtain more objective, data-driven estimates of the EEG and ERPs. PCA was used here to reassess resting state EEG and go/no-go task ERP data from a previous study (Karamacoska et al., 2017) and the relationships between these measures. Twenty adults had EEG recorded with eyes closed (EC) and eyes open (EO), and as they completed an auditory go/no-go task. Separate EEG and ERP PCAs were conducted on each resting condition and stimulus type. For each state, seven EEG components were identified within the delta-beta frequency range, and six ERP components were obtained for go and no-go stimuli. Within the task, mean reaction time (RT) correlated positively with go P2 amplitude and negatively with P3b positivity. Regressions revealed that greater EC delta-1 amplitude predicted shorter mean RT, and larger alpha-3 amplitude predicted go P3b enhancement. These findings demonstrate the immediate P2 and P3b involvement in decision making and response control and the intrinsic EC delta-1 and alpha-3 amplitudes that underpin these processes.

Publication Date


  • 2019

Citation


  • Karamacoska, D., Barry, R. J. & Steiner, G. Z. (2019). Using principal components analysis to examine resting state EEG in relation to task performance. Psychophysiology, 56 (5), e13327-1-e13327-11.

Scopus Eid


  • 2-s2.0-85059570355

Start Page


  • e13327-1

End Page


  • e13327-11

Volume


  • 56

Issue


  • 5

Place Of Publication


  • United States

Abstract


  • Brain dynamics research has highlighted the significance of the ongoing EEG in ERP genesis and cognitive functioning. Few studies, however, have assessed the contributions of the intrinsic resting state EEG to these stimulus-response processes and behavioral outcomes. Principal components analysis (PCA) has increasingly been used to obtain more objective, data-driven estimates of the EEG and ERPs. PCA was used here to reassess resting state EEG and go/no-go task ERP data from a previous study (Karamacoska et al., 2017) and the relationships between these measures. Twenty adults had EEG recorded with eyes closed (EC) and eyes open (EO), and as they completed an auditory go/no-go task. Separate EEG and ERP PCAs were conducted on each resting condition and stimulus type. For each state, seven EEG components were identified within the delta-beta frequency range, and six ERP components were obtained for go and no-go stimuli. Within the task, mean reaction time (RT) correlated positively with go P2 amplitude and negatively with P3b positivity. Regressions revealed that greater EC delta-1 amplitude predicted shorter mean RT, and larger alpha-3 amplitude predicted go P3b enhancement. These findings demonstrate the immediate P2 and P3b involvement in decision making and response control and the intrinsic EC delta-1 and alpha-3 amplitudes that underpin these processes.

Publication Date


  • 2019

Citation


  • Karamacoska, D., Barry, R. J. & Steiner, G. Z. (2019). Using principal components analysis to examine resting state EEG in relation to task performance. Psychophysiology, 56 (5), e13327-1-e13327-11.

Scopus Eid


  • 2-s2.0-85059570355

Start Page


  • e13327-1

End Page


  • e13327-11

Volume


  • 56

Issue


  • 5

Place Of Publication


  • United States