In an attempt to investigate the data validity of one of the methods that I use in my PhD dissertation in Information Science, it has become apparent to me that a lack of methodology standards when conducting EEG (Electroencephalography) is the largest contributor to its biased reputation in this field. Here, as the advocate of use of EEG methodology in the field of Information Science, I (briefly) present an overview and argue how this method may be used as a standardized method in the field, as well as in examining various dimensions of cognitive processes.
Recording Brain’s Electrical Signals – EEG
Let me first give a quick overview of EEG brain’s electrical signals and the quantitative aspect of this method. Devices that collect EEG signals, in actuality collect the voltage fluctuations of the ionic current flow changes of the neurons of the brain (Niedermeyer & da Silva, 2004). These fluctuations of the brain signals are divided into six different wave patterns, depending on the frequency. Brain electrical signals are divided into five different brainwave types: delta, theta, alpha, low beta, midrange beta, high beta, and gamma waves. Each of these brainwave levels has its own specific frequency, ranging from 0 to 100 Hz.
The International 10-20 EEG System
The 10-20 system is a well-recognized technique that indicates specific and standardized locations of the scalp for EEG types of experiments (Niedermeyer & da Silva, 2004). The standardized locations on the scalp suggest areas on the scalp where the EEG electrodes can be set. These locations correlate with specific areas on the neocortex.
Fast Fourier Transformation (FFT)
EEG signals, through a series of mathematical functions and filters may be decomposed. Jean Baptiste Fourier (1768-1830), by developing frequency analysis, has contributed to algorithms, such as FFT, which converts time and space to frequencies (and vise versa). One of the most used quantitative measures of EEG signals is through FFT analysis. Contemporary EEG devices, such as EPOC Emotiv neuroheadset, heavily utilize FFT in order to ‘translate’ brain signals into wave lines displayed digitally and visible to the eyes.
I have covered these topics in great detail in the confirmation part my dissertation, which I hopefully get to publish soon!
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