Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Synchronous neuronal responses across subjects is also known as neural reliability. The level of neural reliability evoked by natural stimuli is shown to be a predictor to larger audience...Show moreSynchronous neuronal responses across subjects is also known as neural reliability. The level of neural reliability evoked by natural stimuli is shown to be a predictor to larger audience preferences (Dmochowski et al., 2014). The same authors also proposed the state-of-the-art method for calculating neural reliability in an EEG setting (Dmochowski et al., 2014). However, the method is indirect and rather ad hoc, therefore, some existing alternative methods are proposed as well as an own proposed algorithm of calculating neural reliability. All the different methods are compared by means of a simulation study. Here, the performance is tested in their ability to recover the actual neural reliability in the data, but also their performance in predicting a population measure. Furthermore, wavelet transform as a denoising step in the setting of EEG data is investigated. The results of the simulation study show that Dmochowski and colleagues’ (2014) is performing well on undenoised data and when the relationship between the “true” ISC and buying behaviour is strong. However, the adapted neural reliability method of Hasson and colleagues’ (2004) and originally intended for fMRI studies stands out not only in terms of performance, but also in consistency of performance under different data characteristics, like the strength of the ISC, the signal to noise ratio and the strength of the relation between true ISC and buying behavior. Moreover, this method is also more direct and easier to calculate. The proposed way of denoising by wavelet transform only hurts the performance of the proposed neural reliability methods. It can be concluded that the adapted method of Hasson and colleagues’ (2004) can be recommended both for determining the ISC as the relation between ISC and a population measure.Show less
