Electroencephalography (EEG) is a method that tracks electrical activity of the brain. EEG offers the advantage of being sensitive to millisecond differences and therefore it can provide useful data regarding the time course of neural processing. EEG setups consist of electrodes that are fitted over an individual’s scalp to record low-amplitude electrical brain activity at the surface of the skull.
At present, it is believed that electrical activity in the brain generates at least four distinct rhythms. Brain waves are a continuum from the large, slow delta waves to smaller and faster (i.e., higher frequency) beta waves. Analysis of the amplitude, frequency, and power of neural oscillations within these brainwave rhythms has furthered our understanding of human cognitive architecture and interaction between its components during cognitive tasks.
Changes in alpha, theta, and gamma waves specifically have been linked to learning related processes such working memory recycling and maintenance. For example, one recent study shows that theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding (Khader, Jost, Ranganath, & Rosler, 2010). Although the drawbacks of EEG (poor spatial resolution, high susceptibility to electrical noise, and the necessity to conduct multiple trials to isolate the brain activity of interest) limit its utility, recent advances in signal processing and electrode headset design, including wireless and dry EEG, have expanded its range of applications to include research in natural settings like the classroom.