An Event Based Topic Learning Pipeline for Neuroimaging Literature Mining

Yan, Jianzhuo; Chen, Jianhui; Sheng, Ying; Xu, Zhe; Mahmud, Mufti

doi:10.21203/rs.3.rs-95392/v1

Cited by 2 publications

(1 citation statement)

References 24 publications

(30 reference statements)

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“…Several studies have applied machine learning methods to classify and/or predict emotional brain states based on EEG activity [23,24]. For example, Chen et al (2020) designed a neural feedback system to predict and classify anxiety states using EEG signals during the resting state from 34 subjects [25]. Anxiety was calculated using power spectral density (PSD), and then SVM was used to classify anxious and non-anxious states.…”

Section: Related Workmentioning

confidence: 99%

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Rahman

Brown

Mahmud

et al. 2022

Preprint

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Virtual-reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required.In this work, we have explored a series of machine learning models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of machine learning models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of Virtual Reality Exposure Therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety.

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Section: Related Workmentioning

confidence: 99%

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Rahman

Brown

Mahmud

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Deep Learning in Mining Biological Data

et al. 2021

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Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

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An Event Based Topic Learning Pipeline for Neuroimaging Literature Mining

Cited by 2 publications

References 24 publications

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Biofeedback Towards Machine Learning Driven Self-guided Virtual Reality Exposure Therapy Based on Arousal State Detection from Multimodal Data

Deep Learning in Mining Biological Data

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