## 1 Introduction
Some literature addressing graphic symbol recognition also supports cognitive load theory [22; 27; 28]. Although most relevant studies [13; 16; 17; 29] have reported that the variety of symbol components affects graphic symbol recognition and learning, the following questions warrant in-depth attention: (a) whether the complexity of graphic symbols (i.e., the number of symbol components) affects children's concentration when they learn or read PCSs composed of various symbol components, (b) whether graphic symbols with numerous components increase or decrease children's reading concentration, and (c) whether graphic symbols with numerous components affect learning outcomes. Published studies on AAC graphic symbol learning have employed techniques such as video recording, observation of behavior, and computer systems to chronicle the learning reactions of the participants [6]. However, the data collected through the aforementioned methods can only present the participants' responses and reaction times after learning or reading graphic symbols; such data do not demonstrate the learning or reading processes (e.g., the children's concentration) or the association of concentration and reaction time with learning outcomes from reading PCSs and animations composed of various numbers of components. Researchers have focused on this problem for investigations of AAC symbol learning in children. However, conventional behavioral observation and computer documentation cannot demonstrate the children's inner psychological process when they learn AAC symbols. Fortunately, the application of brain research in education has gradually become a trend, and researchers have applied various brain research devices to objectively explore the variance of learning competence. Numerous studies have examined the correlations of electroencephalogram (EEG) data, learning outcomes, and concentration. In recent years, as brainwave-detecting devices have become increasingly lightweight (e.g., Neurosky MindSet and MindWave) and research and development of Brain-Computer Interface (BCI) programming have continued to advance, experts and scholars have increasingly participated in the discipline of brainwave analysis [30; 31]. Medical and educational applications of brainwave measurement have emerged because of the association between brainwaves and human consciousness. From the viewpoint of attention theory and cognitive neuroscience, the importance of studying brainwave intensity is to link the level of mental description of information processing in cognitive science to the anatomical level in neuroscience. A brainwave is quantified as a difference between high and low potentials of an electrophysiological signal in the brain. Generally, brainwaves are measured by connecting electrodes to the scalp. The human brain contains an estimated total of 50-100 billion neurons connecting to up to 1 quadrillion synapses, and the brain passes signals through the electric currents generated by these neuron cells [31]. Brainwaves are loosely classified according to frequency range into four categories,namely alpha, beta, theta, and delta waves. The alpha wave can be easily detected in a relaxed state (e.g., eyes closed, zoning out, and absent-minded), whereas the beta wave tends to be detected in an alert state (e.g., playing sports and concentrated thinking). These two types of brainwaves are the types of brainwaves most relevant to human beings' conscious activities. One can calculate alpha and beta waves to obtain the numerical values of brain concentration and relaxation. The present study employed Neurosky EEG devices and systems to investigate the effects and variance of the number of symbol components on AAC graphic symbols identifying for children with and without developmental delays. The two objectives of the present study are listed as follows: (a) to further understand the factors of AAC graphic symbols learning, and (b) to identify the most suitable number of graphic symbol components for children with developmental delays.