Abstract

Neural signal shapes convey significant information about their generating processes. In this study, we introduce a data-driven methodology to identify sensory and behaviourally-relevant traces within neural responses. We present a phenomenological model that characterises temporal variations in intracranial EEG using eight interpretable parameters: peak time, peak intensity, initial and final baselines, accumulation and depletion period, and their respective concavities. This model effectively captures subtle signal variations, especially in sensory decision-making tasks. By decomposing the signals in this manner, we then conduct a comprehensive brain mediation analysis on iEEG datas shape, pinpointing regions that mediate behavioural processes. Importantly, we can determine which signal dynamics specifically reflect underlying behavioural processes, enhancing the depth of analysis and critique of their role in behaviour. Preliminary applications on a cohort of epileptic patients reveal that our model explains over a third of the signal variance at the trial level across all brain regions. We identified four key regions--encompassing sensory, associative, frontal, and premotor areas--that mediate the impact of task difficulty on reaction time. Notably, in these regions, it was the depletion period, rather than signal amplitude, that correlated with behavioural speed. This study highlights the potential of our approach in providing detailed insights into the neural mechanisms linking stimuli to behaviour.