Brain Wave Analysis Research Articles

2C4-1 Development of a real-time brain wave analysis applicationand consideration of impression evaluation of product images using the developed application

2C4-1 Development of a real-time brain wave analysis applicationand consideration of impression evaluation of product images using the developed application

EmoConnect: Nurturing Trust and Relationship Bonds in Alzheimer’s Conversations

Alzheimer's disease presents a global challenge, affecting the ability of individuals to communicate and trust their caregivers. Effective caregiver interactions are vital for emotional well-being in Alzheimer's patients. However, the progressive nature of the disease often leads to communication barriers and a lack of trust, exacerbated by the absence of objective trust assessment mechanisms. Additionally, patients are unable to recall past conversations. To address these challenges, EmoConnect proposes an innovative ML- based solution. EmoConnect solution involves the development of an ML based application that employs brain wave detection and analysis to objectively assess caregiver trustworthiness during interactions with Alzheimer's patients. It calculates a trustworthiness score, enhancing the quality of caregiving and promoting emotional well- being for both caregivers and patients. Furthermore, it stores the text of conversations alongside trust scores, enabling patients to access and review past interactions.

The rise of online group purchases in the age of pandemics

In the wake of the COVID-19 pandemic, a significant shift in consumer behavior has accelerated the transition towards e-commerce, with a notable surge in online group buying. This research pioneers the integration of the Theory of Planned Behavior with neurophysiological measurements to dissect the complex interplay between product price, risk perception, and consumer trust in influencers on online group buying intentions amidst a global health crisis. Unlike previous studies that primarily rely on self-reported data, this investigation employs brainwave analysis to capture the immediate physiological responses of consumers to pandemic-related stimuli, thereby offering a novel perspective on the underlying psychological processes. Our findings reveal that while traditional factors such as price, quantity, and time continue to influence purchasing attitudes, the pandemic has uniquely modulated these effects, with social influence playing a pivotal role in shaping trust towards influencers and, consequently, group buying intentions. Significantly, the study uncovers that COVID-19's impact, though seemingly negligible in survey responses, exerts a profound influence when assessed through experimental measures. By bridging the gap between theoretical models and empirical neurological evidence, this study not only addresses a critical research void but also enhances our understanding of consumer decision-making in times of uncertainty. This advancement in knowledge contributes to the extant literature by providing a more nuanced and comprehensive analysis of the factors affecting online group buying, offering valuable insights for both academics and practitioners navigating the evolving landscape of e-commerce during and beyond the pandemic era.

LabVIEW based Mental Status Monitoring System for Cognitive Wellness

This paper delves into the analysis of various cognitive states by identifying the percentages of different types of brain waves, like alpha, beta, delta, and theta, present in an individual's EEG recording. These brain waves carry crucial information about an individual's cognitive health, and accurate readings are essential to classify and represent the signals accurately. To achieve this, the project aimed to develop a robust platform that utilizes LabVIEW software to obtain EEG signals and interpret cognitive health. The acquired data is meticulously preprocessed and filtered to obtain clean EEG signals, which are then subjected to several methods to determine the content of different brain waves present in the filtered EEG signals. Based on these values, the cognitive status of an individual can be accurately determined, providing valuable insights into their cognitive health. Understanding cognitive states through brain wave analysis has been an intriguing area of research, utilizing various electroencephalography (EEG) techniques to interpret neural activity. This paper aims to provide a detailed discussion on how cognitive states are mapped and interpreted based on brain waves. By exploring the characteristics of each brain wave frequency band, we elucidate their implications in cognitive processes such as attention, memory, and relaxation. This research aims to contribute in the advancement of cognitive neuroscience by consolidating current knowledge and identifying potential avenues for future research.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.

A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves

Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological phenomenon that depicts how the human brain functions. Brain wave analysis is fundamental in applications like brain-computer interference (BCI), beamforming, sleep analysis, epilepsy detection, and emotion recognition. In real-time applications, the brain wave encounters many physiological and non-physiological artifacts during acquisition. Due to this phenomenon, the analysis method is complicated and obscures the brain wave’s tiny features. This study proposes an intelligent signal enhancement unit (SEU) for processing EEG signals to enable decision-making under certainty. The proposed SEU enables healthcare professionals to analyze high-resolution brain wave components for various applications. A new singular spectrum decomposition (SSD) based on score reconstruction (score RC) is used in the first phase of the SEU to generate the artifact nature, which is then used as a reference signal in the adaptive artifact cancellation (AAC) method. The SSD performs the embedding, decomposition, grouping, and reconstruction procedures to provide the reference signal. A modified Logarithmic Non-Negative Adaptive Learning Algorithm (MLNNAL) is employed in the second stage of the SEU to improve the EEG signal. With the help of this proposed adaptive learning, a system with lower computing complexity which is stable and has non-negative weights can be realized. The adaptive learning algorithm’s weight recursion continually reweights the weight vector for each iteration to eliminate artifacts from contaminated brain waves. Excess mean square error (EMSE), signal to noise ratio improvement (SNRI) and computational cost of the adaptive learning algorithm are used to evaluate how the proposed SEU performs.

Öğrenme Destekleriyle Açınsayıcı Öğrenme Oyunları Tasarlama

Serious games are computer games with learning objectives, and present stimulating contexts with interactive, engaging and immersive activities. The brain wave analysis and the neuro-functional correlates of game-based learning revealed that game-based learning is a powerful tool to enliven processes of learning through providing reward and emotional engagement. Implementing a game design model that effectively incorporates game mechanics and pedagogical mechanics is essential in the development of a serious game as an interactive learning environment. The current literature provides many serious game development framework, however, no single framework is sufficient to cover interdisciplinary field of exploratory serious games. This study aimed to develop a framework for designing exploratory serious games. In order to ensure that learners would attain objectives of the learning unit in the game, the proposed conceptual design framework was based upon (a) Kiili’s experiential gaming model (2005), (b) 4C/ID instructional design model (Van Merrienboer, Clark, &amp; de Croock, 2002), (c) ARCS motivation model (Keller, 1987), and (d) Activity theory (Engeström, 1987). The framework pays particular attention on learning support mechanisms of the game because students may have difficulty in, miss or avoid learning curricular content of an exploratory serious game when they focus on merely game mechanics. Finally, the study also stresses and discusses enhancing players’ flow experience and enriching adaptability of the game through design of task regimes.

Fast Pace Sun Salutations (FPSS) Yoga Postures Influence Brain waves Activity in Addition to Heart Rate Variability Pattern: A Pilot Study

ABSTRACT Background: Sun salutation (Surya namaskar) is an ancient yogic exercise, which is a combination of 12 different postures rhythmically. Yoga practitioners perform this procedure in a slow as well as fast pace manner. Aim: We undertook this study to find out the immediate changes in heart rate variability (HRV) and brain waves spectrum after the practice of fast pace sun salutation (FPSS). Settings and Design: This prospective interventional observational study was conducted on 30 male healthy subjects in the 30–45 years age group from September 2021 to December 2021 after approval from the Institutional Ethics Committee. Participants gave their written consent in the prescribed format. Materials and Methods: Fourteen rounds of FPSS were performed in approximately 10 min under the guidance of a yoga expert. Brain Tap Dinamika Machine (Advanced HRV Test System, Moscow, Russia) was used to assess different HRV and brain waves parameters before and after FPSS. Statistical Analysis Used: Statistical analysis was done by Student’s paired t-test using GraphPad online software. Results: All independent variables of time and frequency domain analysis of HRV were found to be increased after the intervention. A significant upsurge in low frequency (LF)% as compared to high frequency (HF)% and very low frequency (VLF)% showed a relative increase in sympathetic activity. In brain waves, delta, theta, and alpha were decreased, whereas activities of beta and gamma waves were found to be increased. Conclusions: Increase in both LF and HF components showed that both sympathetic and parasympathetic components have increased. Brain wave analysis suggested increased mental alertness after FPSS.

Comparative Analysis of Brain Waves by Applying Deep Learning Algorithms

Abstract: During the Covid-19 lockdown, we invited several students with varying levels of education (High school, Middle school, Undergraduate) to watch an online lecture and we recorded their EEG data and brain waves during the lecture. We began by asking them several questions to understand their knowledge base and picked several videos that they would be able to understand and several videos that they wouldn't be able to understand. We then recorded their EEG data, Brain waves, and added a binary variable that indicated whether the student understood the lecture or not. (1 = Understood the lecture | 0 = Did not understand the lecture). We compiled all of our recordings and appended them to a single dataset (EEG data.csv). We also recorded various details relating to the students and videos used, they can be found in Subject details.csv and Video details.csv.

The Role of Sound on Spirituality in Religious Environments and Islamic Architecture by Using EEG.

This present study aims to investigate the effects of the sound pressure level on mosque users' brain waves in the interior of the Nasir al-Mulk mosque in Shiraz. The research hypothesis is based on the fact that the sound pressure level is significantly related to the spiritual sense in the environmental psychology of the mosque. According, in the first step, a survey method is applied, the population of experts is formed, and sound characteristics are ranked using a questionnaire and Friedman's test. Next, the sound pressure level, which obtained the top rank, is selected to be tested and examined. In the second step, using a laboratory method and a brain wave recording device, 6 sound intensities indices were simulated and prepared in the software to carry out the test. The sound used in the present study is "Adhan" since the case study is an Islamic mosque. The test was done in the laboratory and a quiet room. To do the tests, the subjects were asked to sit and the sound was played through headphones for them. The 360-degree image of the mosque was shown to the subjects through virtual reality glasses, and finally, the data obtained from the brain waves recorded by special devices are prepared for review and analysis. The general results of the first step showed that among the characteristics of sound that are effective in creating/enhancing the spiritual sense in the architecture of mosques, sound pressure level obtains the highest score, followed by sound concept, the amplitude of sound, sound quality, sound source, and sound type, respectively. Also, the general results of the analysis of users' brain waves in the second part showed that sound pressure level (40-45dB) is the most effective level in creating/enhancing a spiritual sense in the Nasir al-Mulk Mosque in Shiraz.

Analyzing Cognitive Load Measurements of the Truck Drivers to Determine Transportation Routes and Improve Safety Driving: A Review Study

As part of the Supply Chain (SC), oftentimes Land Logistic Driver (LLD) are held by various uncontrollable occurrences from the surroundings. This caused the cognitive load of the drivers to become higher, which could potentially affect the performance of the LLD to meet the Key Performance Indicator (KPI) of the SC overall. Not only the performance that is affected, but a higher load also could affect the driving behavior towards negativity, as anger and stress perceived become higher, hence a higher crash possibility. Therefore, the need to study the possibility to measure the cognitive load in a certain route that they are on, so any adjustments could be made during a transport activity, with Electroencephalogram (EEG) used as the means to measure it. This study is done by reviewing 15 available research as references regarding EEG and cognitive load. It is possible to use EEG in measuring cognitive load during driving activity, with the focus area of data gathering on the central lobe, parietal lobe, and temporal lobes, with the data extracted from EEG should use the most accurate classifier that focuses on analyzing beta (β) and alpha (α) band as the significant brain wave of the active state. The possible result of the brain wave analysis could be used to determine whether the current route option is burdening LLDs' cognitive load and should be corrected to improve safety driving. Further inclusion of the analysis result could be incorporated into a set of KPI in measuring SC performance.

The impact of media diversity and cognitive style on learning experience in programming video lecture: A brainwave analysis

The impact of media diversity and cognitive style on learning experience in programming video lecture: A brainwave analysis

Fatigue Detection of XYZ Drivers based on Human Brain Wave EEG Signals

The cause of death due to traffic accidents isnow increasingly common. One of the main factors causing thisaccident is driver fatigue. This can happen because the driveris not aware of his tired mental state. Of course mental fatiguecan cause a lack of concentration while driving. This mentalfatigue can be detected by analyzing the brain waves throughthe EEG signal from the driver. This brain wave analysis canbe done by various methods. In this study, the authorsconducted a brain wave-based detection of mental fatigueusing the Fourier transform and Support Vector Machine. TheEEG signal data will be feature extracted using the FourierTransform. Then, the results of this extraction will be used forthe classification process with the Support Vector Machinemethod. Based on the experimental results, the classification ofmental fatigue using a Support Vector Machine with a linearkernel obtained an average accuracy of 85%.

대중가요 작사교육이 여고생의 스트레스 뇌파 및 정서지능에 미치는 효과

This study aims to validate the necessity and justification of popular song lyric education by verifying the effect of popular song lyric education on stress-related brainwaves and the emotional intelligence of female high school students. Lyrics in popular songs are language and symbols that create meaning based on their interpretation. The meaning is shared with the public in various ways (Choi Sang-jin, 2001). Adolescents, the primary consumers of popular music, also feel more familiar with pop lyrics than other reading materials. They relate to their contents (Gong Gyu-taek &amp; Cho Woon-ah 2016). Adolescents tend to show emotional anxiety, impulsiveness, and duality due to psychological and physiological changes from rapid growth (Park So-young 2017, Kim Hyung-hee 2013). Considering that female students spend more time listening to music than male students (Lee Jung-yoon, 2014:15; Miranda &amp; Claes, 2009:229), popular song lyric education using familiar and preferred elements might be effective for female adolescent high school students. A 10-week-lyric education program was devised based on cognitive apprenticeship theory. It was evaluated for scientific validity by examining high school female students’ emotional intelligence. Stress brainwaves were compared before and after popular song lyric education to confirm differences before and after popular song lyric education. The study were 2nd-grade female high school students at S high school in Seoul. The survey was administered by dividing them into 22 comparison groups who participated in popular song lyric education and 19 who did not. The popular song lyric education was conducted over 10 sessions once a week for 50 minutes from March to July 2021. The collected EEG data used the frequency series power spectrum analysis method by fast Fourier transform (FFT) using linear analysis and statistical processing. The IBM SPSS/WIN Version 25.0 program was used for calculations. Effectiveness differences between the groups before and after popular song lyric education was performed using Mann-Whitney and Wilcoxon Signed rank test. The results showed significant differences in stress brainwaves and emotional intelligence. First, stress brainwave analysis, after popular song lyric education, the values of ‘physical tension and stress left and right’ and psychological distraction and stress left and right’ were decreased. Second, differences in linguistic intelligence were detected between groups before and after popular song lyric education. Finally, significant improvements were shown in the fields of ‘emotional engagement,’ ‘thinking promotion,’ ‘emotional control,’ and ‘emotional utilization,’ among other sub-areas. The study clarifies the role of neuroscience’s exploration of popular music education. However, further research is required to build a meaningful scientific foundation for understanding popular music’s effects on the brain.

Coding peekaboom: a gaming mechanism for harvesting programming concepts.

There are plentiful online programming resources that enable learners to develop an understanding of conceptual knowledge and practical implementation. However, learners, especially novices, often experience difficulties locating the required information to solve the programming problems. Differ from natural language in syntax and convention, answers for programming languages may not be found just by simple text information retrieval. To address this issue, Coding Peekaboom, a game-based tagging was developed to help adequately index the critical concepts of a code segment. An EEG device was applied to measure participants’ mental states to identify their engagement during the gameplay. Study results include the effectiveness of appropriate concepts collected by participants whereas 47.15 concepts were collected on average in a game. The brainwave analysis and the questionnaire results reveal that participants were highly engaged in the tagging task via Coding Peekaboom. Correlations were found between the state of flow and the number of concepts selected, score, and time. Finally, the results of the flow theory and personal traits were reported to reflect the user experiences in the game.

Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making

Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence (AI). If the AI understands the mental state behind a user’s decision, it can learn more appropriate decisions even in unclear situations. We introduce the Brain–AI Closed-Loop System (BACLoS), a wireless interaction platform that enables human brain wave analysis and transfers results to AI to verify and enhance AI decision-making. We developed a wireless earbud-like electroencephalography (EEG) measurement device, combined with tattoo-like electrodes and connectors, which enables continuous recording of high-quality EEG signals, especially the error-related potential (ErrP). The sensor measures the ErrP signals, which reflects the human cognitive consequences of an unpredicted machine response. The AI corrects or reinforces decisions depending on the presence or absence of the ErrP signals, which is determined by deep learning classification of the received EEG data. We demonstrate the BACLoS for AI-based machines, including autonomous driving vehicles, maze solvers, and assistant interfaces.

How to play under pressure: EEG monitoring of mental activation training in a professional tennis player

The aim of this study was to monitor the mental activation training during match pressure imageries using a protocol with (MT) and without mental training (wMT) performed in the office and on the tennis court based on the analysis of heart rate, brain waves and subjective ratings in a professional tennis player with high imagery experience. Results showed that both in the office (MTo/wMTo) and on the court (MTc/wMTc) the tennis player's heart rate increased in the match pressure imagery (I.3-8), being higher in the MTo. It decreased in the pressure imagery using mental tools (I.8-13) in the MT. In the case of brainwaves, beta and gamma waves increased in the match pressure imagery (I.3-8); while beta, gamma, delta and theta waves decreased in the pressure imagery using mental tools (I.8-13), being higher in the office. Entropy decreased in the match pressure imagery (I.3-8), being higher in the MTo. It increased in the pressure imagery using mental tools (I.8-13), being higher in the MTo. Regarding subjective ratings, the tennis player felt the pressure in the match pressure imagery, being higher in MT. In the pressure imagery using mental tools he regulated the activation to feel it at an optimal level (7). In the imagery reality, the olfactory and gustatory dimensions were the most difficult to feel in both imageries.

An Empirical Approach to Analyzing the Effects of Stress on Individual Creativity in Business Problem-Solving: Emphasis on the Electrocardiogram, Electroencephalogram Methodology.

In this study, experiments were conducted on 30 subjects by means of electrocardiogram (ECG) and electroencephalogram (EEG) methodologies as well as a money game to examine the effects of stress on creativity in business problem-solving. The study explained the relationship between creativity and human physiological response using the biopsychosocial model of challenge and threat. The subjects were asked to perform a cognitive mapping task. Based on the brain wave theory, we identified the types of brain waves and locations of brain activities that occurred during the creative problem-solving process in a business environment and studied the effects of stress on creativity. The results of the experiments showed significant differences in creativity in business problem-solving depending on whether or not stress was triggered. Differences were found in the time domain (SDNN, RMSSD) and frequency domain (HF, LF/HF ratio) of heart rates, a physiological stress indicator, between the stress group and the no-stress group. A brain wave analysis confirmed that alpha waves increased in the frontal lobe of the brain during creative business problem-solving but decreased when the subjects were under stress, during which beta waves in the brain increased. This study seeks to examine creativity in business problem-solving by studying the effects of stress on human physiological response and cognitive functions in the hope of providing a new and objective interpretation of existing research results.

ARCHITECTURAL NEUROLOGY

This paper aims to propose the implementation of an innovative digital system for architectural design. This new method, based on the interpretation of human emotions for music composition, and the analysis of dreams, would be able to translate images based on architects’ memories, emotions, and learning to design architectural spaces. If this system is able to build, how will it affect architectural design? Different investigations, studies, and analyses are needed to support and answer this thesis. A discovery of a system for musical composition in London, based on the analysis of neural impulses and brain waves; the comparison between the process used for musical composition and architectural design through the explanation of renowned musicians and architects’ methods; and the study of dreams through brain wave analysis would provide us with results and possible benefits for an easier method to design architectural areas. This implementation will offer three benefits: accelerate the design process, automatically associate images and concepts, and have a pure and natural architectural design. Also, the system, capable of linking different images, concepts, emotions, and experiences, would provide various options of spaces produced by the architect. However, understanding that technology will keep advancing with time, human being labor is always needed to refine the method, and an architect would be needed to define and be in charge of choosing between the different options of spaces that the system produced.

Playing under pressure: EEG monitoring of activation in professional tennis players

The aim of this study was to monitor the activation during a neutral situation imagery (NSI) and a pressure situation imagery (PSI), based on the analysis of heart rate, brain waves and subjective ratings in athletes. The sample was made up of sixteen professional tennis players. Imagery protocols consisted of 3 phases; the first and the third involved being focused on their deep breathing (2 min.); the second, in the NSI, first service routine (17 s.-1 min. 21 s.) and in the PSI, a match pressure situation (2 min. 10 s.). Results showed that both NSI and PSI increased heart rate. This increase was higher in the PSI and its highest point was at the maximum pressure moment: interval 5–6. In the case of brain waves, both NSI and PSI caused a decrease in gamma wave activity (intervals 3–8). In the PSI, there was also an increase in gamma waves in interval 5–6, the maximum pressure moment. Entropy was lower in the NSI. In regard to subjective ratings, in the psychological skills there were only significant differences in the PSI between pre- and post-activation at the during moment (pressure). In the imagery reality, olfactory and gustatory dimensions were the most difficult to perceive as real in the NSI and only the olfactory in the PSI.