Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity

Music has many awe-inspiring characteristics. Some may refer to it as a “universal language” with the ability to transcend the barriers of speech, while others may describe its ability to evoke intense emotional experiences for the listener. Regardless of the description, it is a commonly held view that music can have many profound effects. Studies of music’s effects have found these beliefs to be more than pure conjecture, finding that music interacts with and changes our brains in physical and emotional ways. Music can even have clinical applications, such as music therapy. This type of therapy has been shown to be beneficial in many areas, ranging from stroke rehabilitation to mental health treatment. The mechanisms behind music’s therapeutic benefit has to do with neuroplastic effects; Being able to harness this benefit in a therapeutic setting could make treatments for mental disorders and brain injuries even more effective. This thesis aimed to discover whether musical thoughts could be interpreted using machine learning, potentially opening the door to the use of thought-based musical training for therapeutic benefit. For this study, EEG data was collected while people were thinking of 5 melodies, then machine learning models were trained on labeled datasets. The models were then tasked with categorizing unlabeled sets of EEG data - in other words, predicting which melody a subject was thinking of while the data was being recorded. The accuracy of the predictions ranged from 45% to 80%, which means that the programs were 2-4 times more accurate than random guessing. This shows that these programs could potentially be used to examine the effects of musical thinking on neuroplasticity. While this topic is still exploratory and requires more research, these results could lead to a promising future of development of music-based brain-computer interfaces

Emotion And Cognition Analysis Of Intro And Senior CS Students In Software Engineering

he software engineering community has advanced the field in the past few decades towards making the software development life cycle more efficient, robust, and streamlined. Advances such as better integrated development environments and agile workflows have made the process more efficient as well as more flexible. Despite these many achievements software engineers still spend a great deal of time writing, reading and reviewing code. These tasks require a lot of attention from the engineer with many different variables affecting the performance of the tasks. In recent years many researchers have come to investigate how emotion and the way we think about code affect our ability to write and understand another’s code. In this work we look at how developers’ emotions affect their ability to solve software engineering tasks such as code writing and review. We also investigate how and to what extent emotions differ with the software engineering experience of the subject. The methodologies we employed utilize the Emotiv Epoc+ to take readings of subjects’ brain patterns while they perform code reviews as well as write basic code. We then examine how the electrical signals and patterns in the participants differ with experience in the field, as well as their efficiency and correctness in solving the software engineering tasks. We found in our study that senior students had much smaller distribution of emotions than novices with a few different emotion groups emerging. The novices, while able to be grouped, had a much wider dispersion of the emotion aspects recorded

Wireless Sensors for Brain Activity—A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.</jats:p

Wireless Sensors for Brain Activity — A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation

Beyond Detection: Investing in Practical and Theoretical Applications of Emotion + Visualization

Emotion is a dynamic variable that modulates how we perceive, reason about, and interact with our environment. Recent studies have established that emotion’s influence carries to data analysis and visualization, impacting performance in ways both positive and negative. While we are still in the infancy of understanding the role emotion plays in analytical contexts, advances in physiological sensing and emotion research have raised the possibility of creating emotion-aware systems. In this position paper, we argue that it is critical to consider the potential advances that can be made even in the face of imperfect sensing, while we continue to address the practical challenges of monitoring emotion in the wild. To underscore the importance of this line of inquiry, we highlight several key challenges related to detection, adaptation, and impact of emotional states for users of data visualization systems, and motivate promising avenues for future research in these areas

Neuromarketing: a review of research and implications for marketing

In this research, we reviewed existing studies which used neuromarketing techniques in various fields of research. The results revealed that most attempts in neuromarketing have been made for business research. This research provides important results on the use of neuromarketing techniques, their limitations and implications for marketing research. We hope that this research will provide useful information about the neuromarketing techniques, their applications and help the researchers in conducting the research on neuromarketing with insight into the state-of-the-art of development methods