Investigation of EEG Alpha Rhythm of Artists and Nonartists During Visual Perception, Mental Imagery, and Rest

Abstract

In this article, differences in alpha power between multichannel EEG signals of artists and nonartists were investigated. The two groups were compared during visual perception, mental imagery, and at-rest conditions. We found no significant differences between the two groups in absolute alpha power at rest. Both absolute and relative alpha power were found to be significantly lower in artists during the visual perception and the mental imagery tasks for some of the channels, suggesting that the cerebral efforts of artists were higher during these conditions. These results indicate that artists considered more features of a painting compared to nonartists. The two groups differed in relative alpha power during the visual perception task compared to the at-rest condition. Relative alpha power values decreased during the visual perception for both groups; however, their variation patterns were different. More significant decreases in relative upper alpha power (10-12Hz) during the visual perception task for nonartists were observed, whereas more significant decreases in relative lower alpha power (8-10Hz) for artists during the visual perception task. These differences may be employed to measure progress in novice artists. In addition, it was found that upper phasic alpha power values were higher in artists compared to nonartists during the visual perception task. In the mental imagery task, lower phasic alpha power values were found to be higher in nonartists. However, the differences between the two groups were not significant in all of the channels indicating that EEG signals do not need to recorded in all channels when determining artistic expertise by absolute, relative, or phasic alpha power.

Analysis of EEG Signals Related to Artists and Nonartists during Visual Perception, Mental Imagery, and Rest Using Approximate Entropy

“…In addition, artists can mentally visualize a painting more capably due to their improved visual perception. This is because they know art and its significant characteristics [ 5 ]. Therefore, artists process more information during visual perception and mental imagery than nonartists, and increasing ApEn is reasonable.…”

“…In this approach, a novice had to carry out the four visual perception tasks and significant changes in ApEn were measured. The progress of the participant is quantified by comparing the measured significant variation pattern to those obtained by expert artists while carrying out the similar tasks [ 5 ].…”

“…They found that the two groups are distinguishable at rest using scaling exponents; however, a decrease in average classification accuracy is observed for classifying the two groups when performing the same cognitive tasks [ 21 ]. A significant decrease in alpha wave activity for artists as compared to nonartists during visual perception and mental imagery has also been observed [ 5 ]. In addition, it has been found that the two groups are distinguishable using wavelet coefficients [ 22 ].…”

“…We suggested investigating differences between multichannel EEG signals of artists and nonartists. The differences between the two groups were explored to date using some features such as scaling exponent, alpha power, and wavelet coefficients [ 5 , 19 – 22 ]. However, there was no broad research that investigated differences between the two groups in entropy based features such as ApEn.…”

A Comparative Investigation of Wavelet Families for Analysis of EEG Signals Related to Artists and Nonartists During Visual Perception, Mental Imagery, and Rest

“…These studies found that the patterns of cortical activity of experts and nonexperts are different (Abernethy & Russell, 1987;Bhattacharyaa & Petsche, 2002;Bird, 1987;Collins, Powell, & Davies, 1990;Crews & Landers, 1993;Fink, Graif, & Neubauer, 2009;Hatfield, Landers, & Ray, 1984;Haufler, Spalding, Maria, & Hatfield, 2000;Karkare, Saha, & Bhattacharya, 2009;Panga, Nadalb, Müllerc, Rosenbergd, & Kleine, 2012;Petsche, Lindner, Rappelsberger, & Gruber, 1988;Petsche, Richter, Stein, Etlinger, & Filz, 1993;Radlo, Steinberg, Singer, Barba, & Melinkov, 2002;Salazar et al, 1990;Shourie, Firoozabadi, & Badie, 2011, 2013Wagner, 1975aWagner, , 1975b. Most of the previous research has focused on the power spectrum density of the traditional EEG rhythms, which may not be reliable due to the nonstationary nature of the EEG signals (Collins et al, 1990;Crews & Landers, 1993;Fink et al, 2009;Hatfield et al, 1984;Haufler et al, 2000;Petsche et al, 1988;Petsche et al, 1993;Radlo et al, 2002;Salazar et al,1990;Shourie et al, 2013;Wagner, 1975aWagner, , 1975b. Hence, time-frequency features are highly reliable for EEG signal analysis.…”