Computational protocol: EEG Correlates of the Flow State: A Combination of Increased Frontal Theta and Moderate Frontocentral Alpha Rhythm in the Mental Arithmetic Task

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Protocol publication

[…] As for performance data, the percentage of correct answers and response time were averaged for each task condition for each participant. The effect of the task condition on these two types of performance data was examined using analysis of variance.Regarding subjective experience, rating scores on each item of the questionnaire measuring the state of flow were averaged across the three blocks of each task condition. Differences between each pair of task conditions were examined for each item using analysis of variance. Furthermore, to investigate the aspects of flow measured by the employed questionnaire, a factor analysis was performed on the rating data.EEG data were analyzed using EEGLAB (Version 13.4.4b) and FieldTrip (Build 20140522) working on MATLAB (Version R2013b, the MathWorks, Inc., Massachusetts, USA). The EEG data were down-sampled to 256 Hz, filtered with 1 and 100 Hz bandpass. An independent component analysis was performed on the EEG data to identify independent components corresponding to noises that were to be manually rejected using visual inspection. Additionally, the Laplacian filter was applied. Each trial was extracted from the preprocessed data, and trials including eye blinks and noisy channels where potentials exceeded ±100 μV were discarded. The averaged rejection rate was 2.2% (SD = 4.2) for all the participants.A time-frequency analysis was applied to the data from each task block. The analysis was performed using Morlet wavelet analysis available on the FieldTrip toolbox. A total of 7 cycles determining the width of wavelets was used. In this analysis, the wavelet's center frequencies range from 1 to 100 Hz in steps of 1 Hz, and the time window was moved from 10 s before to 25 s after the trial onset in steps of 5 ms. The analyzed data were averaged for each task condition for each participant.Based on the averaged data, four frequency ranges were extracted (delta: 1–3 Hz, theta: 4–7 Hz, alpha: 10–13 Hz, beta: 14–30 Hz). Separate ROI analyses were conducted focusing seven areas (Frontocentral: AFz, Fz, FCz; Left frontal: AF3, F3, F7, FC3; Right frontal: AF4, F4, F8, FC4; Left central: C3, C5, CP3, CP5; Right central: C4, C6, CP4, CP6; Left occipital: P3, P5, PO3, PO7; Right occipital: P4, P6, PO4, PO8) for each frequency range. The data of each trial included the EEG data after answering the mathematical problem. To focus on EEG activity while solving the computational problem, amplitudes during the interval from 1 s after the trial onset to average response time in each condition (Boredom condition: 5.5 s, Flow condition: 14.8 s, Overload condition: 17.2 s after the trial onset) were averaged for each area for each frequency range. The amplitudes in each task condition were assessed in terms of the amount of changes based on the data from the Rest blocks. To test the effect of task condition on amplitudes, analysis of variance was performed. […]

Pipeline specifications

Software tools EEGLAB, FieldTrip
Application Clinical electrophysiology
Organisms Homo sapiens