ABSTRACT
Frequency-based, or quantitative, electroencephalography (qEEG) is an analytical approach in which EEG signals are decomposed into different oscillatory frequencies. qEEG features, such as the relative proportion of the signal captured by each frequency (power) or the degree of similarity in signal composition across electrodes over time (coherence), are then used to characterize individual differences in neural processing. This chapter focuses on describing this method, and how it has been leveraged to study questions pertaining to second language learning. Specifically, the chapter examines how both intrinsic (task-free) and dynamic (task-based) qEEG measures predict second language learning success (second language aptitude) and are modulated by past second language experiences. Together, these converging lines of work implicate communication in the beta frequency band as an index of second language aptitude and suggest that neural network configuration becomes more focal with increased second language experience.
