Browsing by Author "King, Jung-Tai"

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A Wireless Multifunctional SSVEP-Based Brain-Computer Interface Assistive System
(2019-09-01)
Lin, Chin-Teng
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Chiu, Ching-Yu
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Singh, Avinash Kumar
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King, Jung-Tai
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Ko, Li-Wei
Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their nonportability, impracticality, and inconvenience because of the adoption of wired or invasive electroencephalography acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this paper, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: 1) video entertainment; 2) video calling; and 3) active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate achieved is 90.91% and 24.94 bit per min, respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
A Wireless Steady State Visually Evoked Potential-based BCI Eating Assistive System
(2017-01-01)
Chiu, Ching-Yu
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Singh, Avinash K.
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Wang, Yu-Kai
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King, Jung-Tai
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Lin, Chin-Teng
Brain-Computer interface (BCI) which aims at enabling users to perform tasks through their brain waves has been a feasible and worth developing solution for growing demand of healthcare. Current proposed BCI systems are often with lower applicability and do not provide much help for reducing burdens of users because of the time-consuming preparation required by adopted wet sensors and the shortage of provided interactive functions. Here, by integrating a state visually evoked potential (SSVEP)-based BCI system and a robotic eating assistive system, we propose a non-invasive wireless steady state visually evoked potential (SSVEP)-based BCI eating assistive system that enables users with physical disabilities to have meals independently. The analysis compared different methods of classification and indicated the best method. The applicability of the integrated eating assistive system was tested by an Amyotrophic Lateral Sclerosis (ALS) patient, and a questionnaire reply and some suggestion are provided. Fifteen healthy subjects engaged the experiment, and an average accuracy of 91.35%, and information transfer rate (ITR) of 20.69 bit per min are achieved. For online performance evaluation, the ALS patient gave basic affirmation and provided suggestions for further improvement. In summary, we proposed a usable SSVEP-based BCI system enabling users to have meals independently. With additional adjustment of movement design of the robotic arm and classification algorithm, the system may offer users with physical disabilities a new way to take care of themselves.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2015)
Cherng, Fu-Yin
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Lin, Wen-Chieh
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King, Jung-Tai
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Lee, Yi-Chen
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資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EEG-based Approach for Evaluating Graphic Icons from the Perspective of Semantic Distance
(2016)
Cherng, Fu-Yin
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Lin, Wen-Chieh
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King, Jung-Tai
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Lee, Yi-Chen
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資訊工程學系
Graphic icons play an increasingly important role in interface design due to the proliferation of digital devices in recent years. Their ability to express information in a universal fashion allows us to immediately interact with new applications, systems, and devices. Icons can, however, cause user confusion and frustration if designed poorly. Several studies have evaluated icons using behavioral-performance metrics such as reaction time as well as self-report methods. However, determining the usability of icons based on behavioral measures alone is not straightforward, because users\' interpretations of the meaning of icons involve various cognitive processes and perceptual mechanisms. Moreover, these perceptual mechanisms are affected not only by the icons themselves, but by usage scenarios. Thus, we need a means of sensitively and continuously measuring users\' different cognitive processes when they are interacting with icons. In this study, we propose an EEG-based approach to icon evaluation, in which users\' EEG signals are measured in multiple usage scenarios. Based on a combination of EEG and behavioral results, we provide a novel interpretation of the participants\' perception during these tasks, and identify some important implications for icon design.
An EEG-Based Fatigue Detection and Mitigation System
(2016-06)
Huang, Kuan-Chih
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Huang, Teng-Yi
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Chuang, Chun-Hsiang
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King, Jung-Tai
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Wang, Yu-Kai
Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual\'s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant\'s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject\'s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements
(2016-07)
Yu, Yi-Hsin
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Lu, Shao-Wei
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Chuang, Chun-Hsiang
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King, Jung-Tai
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Chang, Che-Lun
Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.
Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements
(2020-04-01)
Lin, Chin-Teng
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Yu, Yi-Hsin
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King, Jung-Tai
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Liu, Chi-Hsien
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Liao, Lun-De
The aim of this study was to develop a novel dry electroencePhalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull providing a suitable surface contact area for collecting elec: trical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biognal measurements, we proposed a new, augmented weirle-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, thisi faugm!nted WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is easiblefor practical applications.
Brain Electrodynamic and Hemodynamic Signatures Against Fatigue During Driving
(2018-03-27)
Chuang, Chun-Hsiang
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Cao, Zehong
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King, Jung-Tai
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Wu, Bing-Syun
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Wang, Yu-Kai
Fatigue is likely to be gradually cumulated in a prolonged and attention-demanding task that may adversely affect task performance. To address the brain dynamics during a driving task, this study recruited 16 subjects to participate in an event-related lane-departure driving experiment. Each subject was instructed to maintain attention and task performance throughout an hour-long driving experiment. The subjects' brain electrodynamics and hemodynamics were simultaneously recorded via 32-channel electroencephalography (EEG) and 8-source/16-detector functional near-infrared spectroscopy (fNIRS). The behavior performance demonstrated that all subjects were able to promptly respond to lane-deviation events, even if the sign of fatigue arose in the brain, which suggests that the subjects were fighting fatigue during the driving experiment. The EEG event-related analysis showed strengthening alpha suppression in the occipital cortex, a common brain region of fatigue. Furthermore, we noted increasing oxygenated hemoglobin (HbO) of the brain to fight driving fatigue in the frontal cortex, primary motor cortex, parieto-occipital cortex and supplementary motor area. In conclusion, the increasing neural activity and cortical activations were aimed at maintaining driving performance when fatigue emerged. The electrodynamic and hemodynamic signatures of fatigue fighting contribute to our understanding of the brain dynamics of driving fatigue and address driving safety issues through the maintenance of attention and behavioral performance.
Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data
(2018-11-12)
Fonseca, Andre
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Kerick, Scott
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King, Jung-Tai
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Lin, Chin-Teng
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Jung, Tzyy-Ping
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems
(2019-11-01)
Lin, Chin-Teng
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Liu, Chi-Hsien
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Wang, Po-Sheng
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King, Jung-Tai
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Liao, Lun-De
A brain-computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.
EEG Alpha and Gamma Modulators Mediate Motion Sickness-Related Spectral Responses
(2016-03)
Chuang, Shang-Wen
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Chuang, Chun-Hsiang
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Yu, Yi-Hsin
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King, Jung-Tai
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Lin, Chin-Teng
Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha (r = 0.421) and gamma (r = 0.478) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.
EEG-based Measures of Auditory Saliency in a Complex Context
(2019-01-01)
Huang, Xun-Yi
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Cherng, Fu-Yin
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King, Jung-Tai
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Lin, Wen-Chieh
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資訊工程學系
Auditory saliency is an important mechanism that helps humans extract relevant information from environments. Audio notifications of mobile devices with high saliency can increase users' receptivity, yet overly high saliency could cause annoyance. Accurately measuring auditory saliency of a notification is critical for evaluating its usability. Previous studies adopted behavioral methods. However, their results may not accurately reflect auditory saliency as humans' perception of auditory saliency often involves complicated cognitive processes. Thus, we propose an electroencephalography (EEG)-based approach that can complement behavioral studies to provide a more nuanced analysis of auditory saliency. We evaluated our method by conducting an EEG experiment that measured the mismatch negativity and P3a of the sounds in realistic scenarios. We also conducted a behavioral experiment to link the EEG-based method with the behavioral method. The results suggested that EEG can provide detailed information about how human perceive auditory saliency and complement the behavioral measures.
Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements
(2020-01-01)
Lin, Chin-Teng
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King, Jung-Tai
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Chuang, Chun-Hsiang
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Ding, Weiping
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Chuang, Wei-Yu
Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO(2)) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO(2) decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO(2) oscillations suggest that activations in HbO(2) are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.
Extracting Patterns of Single-Trial EEG Using an Adaptive Learning Algorithm
(2015)
Lin, Chin-Teng
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Wang, Yu-Kai
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Fang, Chieh-Ning
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Yu, Yi-Hsin
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King, Jung-Tai
The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.
Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
(2020-01-01)
Cao, Zehong
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Lin, Chin-Teng
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Lai, Kuan-Lin
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Ko, Li-Wei
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King, Jung-Tai
Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 $ \pm $ 6 & x0025; and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.
Forehead EEG in support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment
(2017-01-01)
Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Cao, Zehong
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Singh, Avinash Kumar
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Hung, Chih-Sheng
There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.
How Does Gender Stereotype Affect the Memory of Advertisements? A Behavioral and Electroencephalography Study
(2020-07-14)
Lo, Shih-Yu
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King, Jung-Tai
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Lin, Chin-Teng
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傳播研究所
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通識教育中心
Previous studies have shown equivocal results about whether atypical or unusual events, compared with typical ones, facilitate or inhibit memory. We suspect that the indefinite findings could be partly due to the recall task used in these studies, as the participants might have used inference instead of recall in their responses. In the present study, we tested the recognition memory for real (Experiment 1) and fabricated (Experiment 2) advertisements, which could be congruent or incongruent with gender stereotypes. In congruent advertisements, a female endorser presented a traditionally considered feminine product or a male endorser presented a traditionally considered masculine product, whereas the gender-product type matching reversed in incongruent advertisements. The results of both behavioral experiments revealed that the participants' memory performance for stereotype-incongruent advertisements was higher than for congruent ones. In the event-related potential (ERP) recordings in Experiment 3, larger positive amplitudes were found for stereotype-incongruent advertisements than for congruent advertisements on the left parietal sites, suggesting a deeper encoding process for stereotype-incongruent information than for stereotype-congruent information.
Imagining Garage Start-Ups: Interactive Effects of Imaginative Capacities on Entrepreneurial Intention
(2016-07-09)
Chang, Chi-Cheng
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Yao, Shu-Nung
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Chen, Shi-An
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King, Jung-Tai
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Liang, Chaoyun
This article describes a structural examination of the interaction among different imaginative capacities and the entrepreneurial intention of electrical and computer engineering students. Two studies were combined to confirm the factor structure of survey items and test the hypothesised interaction model. The results indicated that imaginative capacity has 3 dimensions: conceiving, initiating, and transforming, whereas entrepreneurial intention comprises 2 factors: conviction and preparation. In addition, the results indicated that initiating imagination strongly affected entrepreneurial conviction and preparation; conceiving imagination positively predicted entrepreneurial conviction, whereas transforming imagination negatively predicted entrepreneurial preparation. The results also indicated that students possessing high, as well as those possessing low, conceiving and transforming imaginations demonstrated a high level of entrepreneurial preparation. In addition, an increase in transforming imagination increases the preparation of students possessing low initiating imagination and reduces the preparation of students possessing high initiating imagination. For developing a novel approach to conduct research on entrepreneurial intention and for discussing the practical implications of the findings, this study explicates alternative methods of cultivating students, promoting their interests, and enabling entrepreneurial behaviour in them by stimulating their imaginative capacities.
Infant Brain Detects Inconsistency Between Belief and Action: An Electroencephalography Study
(2019-09-01)
Chou, Yu-Ju
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King, Jung-Tai
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腦科學研究中心
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Brain Research Center
Learning in the visual association of novice and expert designers
(2017-06-01)
Yao, Shu-Nung
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Lin, Chin-Teng
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King, Jung-Tai
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Liu, Yu-Cheng
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Liang, Chaoyun
Designers are adept at determining similarities between previously seen objects and new creations using visual association. However, extant research on the visual association of designers and the differences between expert and novice designers when they engage in the visual association task are scant. Using electroencephalography (EEG), this study attempted to narrow this research gap. Sixteen healthy designers-eight experts and eight novices-were recruited, and asked to perform visual association while EEG signals were acquired, subsequently analysed using independent component analysis. The results indicated that strong connectivity was observed among the prefrontal, frontal, and cingulate cortices, and the default mode network. The experts used both hemispheres and executive functions to support their association tasks, whereas the novices mainly used their right hemisphere and memory retrieval functions. The visual association of experts appeared to be more goal-directed than that of the novices. Accordingly, designing and implementing authentic and goal-directed activities for improving the executive functions of the prefrontal cortex and default mode network are critical for design educators and creativity researchers. (C) 2017 Elsevier B.V. All rights reserved.