TFR test

What type of data is required to compute a TFR in MNE-Python?

• Raw EEG/MEG data

• Epochs object

• CA components

• Source estimates

What is the purpose of using a baseline correction in TFR analysis?

• To increase the power of high-frequency components

• To normalize the power across different channels

• To remove power fluctuations unrelated to experimental conditions

• To apply a bandpass filter

What is the trade-off when selecting the number of cycles in a Morlet wavelet?

• More cycles improve time resolution but reduce frequency resolution

• More cycles improve frequency resolution but reduce time resolution

• More cycles improve both time and frequency resolution

• The number of cycles has no impact on resolution

What is the effect of using a broader frequency bandwidth in TFR analysis?

• Increases time resolution but reduces frequency resolution

• Increases frequency resolution but reduces time resolution

• Improves both time and frequency resolution

• Has no effect on the resolution

What does the freqs parameter specify in tfr_multitaper and tfr_morlet functions?

• Time intervals for analysis

• Frequency bands to analyze

• Number of tapers used

• Number of epochs included

Which parameter controls the number of cycles per frequency in the TFR analysis?

• n_cycles

• t_min

• n_permutations

• bandwidth

In baseline correction for TFR analysis, what does the "percent" mode represent?

• The relative percentage change in power compared to the baseline

• The logarithmic ratio of power before and after baseline

• The z-scored transformation of power values

• The absolute power values without any normalization

What is a common approach to test statistical significance in TFR data?

• Permutation-based cluster statistics

• Applying a simple t-test on power values

• Computing the mean absolute deviation

• Running an ANOVA on individual time points

How is ERDS (Event-Related Desynchronization/Synchronization) calculated?

• By directly comparing raw EEG signals before and after an event

• By computing power changes relative to a baseline period

• By applying Independent Component Analysis (ICA)

• By filtering the EEG signal below 1 Hz

What is the key difference between Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS)?

• ERD represents a decrease in power due to cortical activation, while ERS represents an increase in power often linked to inhibition or idling states

• ERD is observed only in low-frequency bands, while ERS occurs only in high-frequency bands

• ERD and ERS both indicate increased neural activity but in different brain regions

• ERD is only present in resting-state EEG, while ERS occurs only during active tasks