ORCID Profile
0000-0001-9930-241X
Current Organisations
Australian National University
,
CSIRO Black Mountain Laboratories
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CSIRO Health and Biosecurity
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Publisher: MDPI AG
Date: 04-05-2022
DOI: 10.3390/MTI6050035
Abstract: Music elicits strong emotional reactions in people, regardless of their gender, age or cultural background. Understanding the effects of music on brain activity can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research. We explore the effects of three different music genres on people’s cerebral hemodynamic responses. Functional near-infrared spectroscopy (fNIRS) signals were collected from 27 participants while they listened to 12 different pieces of music. The signals were pre-processed to reflect oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentrations in the brain. K-nearest neighbor (KNN), random forest (RF) and a one-dimensional (1D) convolutional neural network (CNN) were used to classify the signals using music genre and subjective responses provided by the participants as labels. Results from this study show that the highest accuracy in distinguishing three music genres was achieved by deep learning models (73.4% accuracy in music genre classification and 80.5% accuracy when predicting participants’ subjective rating of emotional content of music). This study validates a strong motivation for using fNIRS signals to detect people’s emotional state while listening to music. It could also be beneficial in giving personalised music recommendations based on people’s brain activity to improve their emotional well-being.
Publisher: JMIR Publications Inc.
Date: 02-03-2023
Abstract: omputational signal pre-processing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility which will drive clinical adoption. his review focuses on the Neonatal intensive care unit (NICU) setting and summarises the state-of-the-art computational methods used for pre-processing neonatal clinical physiological signals for the development of machine learning models in predicting the risk of adverse outcomes. ive databases (Pubmed, Web of Science, Scopus, IEEE, ACM Digital Library) were searched using a combination of keywords and MeSH terms. 3,585 papers from the year 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2,994 papers were screened by title and abstract, and 81 were selected for full-text review. Of these, 52 were eligible for inclusion in the detailed analysis. he papers included in the review were heterogeneous in design and the selection of adverse outcomes modelled. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal pre-processing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. he review found heterogeneity in techniques and inconsistent reporting of parameters and procedures used for pre-processing neonatal physiological signals, which is necessary to confirm their adherence to clinical practices, usefulness and choice of the best practices. Improving this aspect will ensure transparent reporting and hence facilitate the interpretation and reproducibility of the studies as well as accelerate their clinical adoption. /A
Publisher: MDPI AG
Date: 28-12-2022
DOI: 10.3390/APP13010387
Abstract: Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we sub ided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the in idual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring.
No related grants have been discovered for Jessica Sharmin Rahman.