[Eeglablist] EEG sensitivity to rapid learning in degraded speech

[Shada Nassar]

Dear EEGLAB community,

I’m a PhD student designing (my first) EEG study on rapid adaptation to challenging speech, and I’d really appreciate advice on both study design and analysis strategies that are feasible with short exposure and variable sentence stimuli.

I’m not aiming to measure long-term plasticity, but rather neural markers of rapid within-session improvement during degraded speech recognition—markers consistent with increased processing efficiency / improved prediction / auditory–linguistic integration.

Current plan:

64-channel EEG

Within-session trial-by-trial change over a brief exposure phase (~a few minutes)

~30 trials per condition (trying to keep exposure minimal to target “rapid” learning)

Conditions: speech-in-noise, time-compressed, and vocoded speech (within-subject; order can be counterbalanced)

Stimuli: sentences; each sentence is different (no repetition)

Task: transcribe the final word of each sentence (no feedback)

I’m considering using the N400 to sentence-final words as a potential marker of improved prediction/integration


My main concerns are given (1) short exposure, (2) variable sentences, and (3) limited trials per condition, I’m unsure whether EEG will be sensitive enough to detect meaningful learning-related changes, and I’d love guidance on how to design/analyze this.

Is this design realistic for detecting a rapid-learning EEG signature, or is there any design changes you would recommend?

Given the constraints, is the N400 a reasonable target, or are other measures more robust?

What’s the best way to model rapid learning—early vs. late bins, continuous trial-wise slopes, or accuracy-linked single-trial analyses?

Any best practices for separating learning from fatigue or attention drift in short exposure designs?



Thank you for any guidance,
Shada