KEDRI - the Knowledge Engineering and Discovery Research Institute
Permanent link for this collectionhttps://hdl.handle.net/10292/552
KEDRI - the Knowledge Engineering and Discovery Research Institute of Auckland University of Technology was established in June 2002 and since then has been developing novel information processing methods, technologies and their applications to enhance discoveries across different areas of science and engineering. The methods are mainly based on principles from Nature, such as brain information processing, evolution, genetics, quantum physics.
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Item A Scoping Review of Tinnitus Research Undertaken by New Zealand Researchers: Aotearoa – An International Hotspot for Tinnitus Innovation and Collaboration(Informa UK Limited, 2024-07-03) Searchfield, Grant; Adhia, Divya; Barde, Amit; De Ridder, Dirk; Doborjeh, Maryam; Doborjeh, Zohreh; Goodey, Ronald; Maslin, Michael RD; Sanders, Phil; Smith, Paul F; Zheng, YiwenTinnitus is a very common oto-neurological disorder of the perception of sound when no sound is present. To improve understanding of the scope, strengths and weaknesses of New Zealand tinnitus research, a critical scoping review was undertaken. The aim was to help develop priorities for future research. A review of the literature was undertaken using a 6-stage scoping review framework of Scopus and Pub Med were searched in May 2023 with the combination of following key word [Tinnitus] and country of affiliation [New Zealand]. The search of PubMed resulted in 198 articles and that of Scopus 337 articles. After initial consideration of title relevance to the study (165 from PubMed and 196 from Scopus) removal of duplicates and after reading the articles and adding from references, 208 studies were chosen for charting of data. Nine themes were identified and described: A. Epidemiology; B. Models; C. Studies in animals; D. Mechanisms; E. Assessment and prognosis; F. Pharmacotherapy; G. Neuromodulation; H. Sensory therapies; I. Clinical practice. An urgent priority for future tinnitus research in NZ must be to address the absence of cultural and ethnic diversity in participants and consideration of traditional knowledge.Item A Hybrid Spiking Neural Network - Quantum Framework for Spatio-Temporal Data Classification: A Case Study on EEG Data(SpringerOpen, 2025-11-11) Jha, Ravi Kumar; Kasabov, Nikola; Bhattacharyya, Saugat; Coyle, Damien; Prasad, GirijeshThe study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNNs) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models for spatio-temporal data, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum computation (SNN-QC) framework, SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilising feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel high-order nonlinear feature map that demonstrates improved performance over state-of-the-art feature maps and several machine learning methods across most of the tasks studied. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its efficacy across multiple binary classifiers. Quantum hardware validation is conducted using both a superconducting IBM-QPU and a high-fidelity noisy simulation that replicates a real QPU. Furthermore, the results demonstrate that the SNN-QC outperforms models that use statistical features rather than features extracted from the SNN, as the SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data.Item Review of Deep Learning Models With Spiking Neural Networks for Modeling and Analysis of Multimodal Neuroimaging Data(Frontiers Media SA, 2025-11-14) Khan, Ayesha; Shim, Vickie; Fernandez, Justin; Kasabov, Nikola K; Wang, AlanMedical imaging has become an essential tool for identifying and treating neurological conditions. Traditional deep learning (DL) models have made tremendous advances in neuroimaging analysis; however, they face difficulties when modeling complicated spatiotemporal brain data. Spiking Neural Networks (SNNs), which are inspired by real neurons, provide a promising option for efficiently processing spatiotemporal data. This review discusses current improvements in using SNNs for multimodal neuroimaging analysis. Quantitative and thematic analyses were conducted on 21 selected publications to assess trends, research topics, and geographical contributions. Results show that SNNs outperform traditional DL approaches in classification, feature extraction, and prediction tasks, especially when combining multiple modalities. Despite their potential, challenges of multimodal data fusion, computational demands, and limited large-scale datasets persist. We discussed the growth of SNNs in analysis, prediction, and diagnosis of neurological data, along with the emphasis on future direction and improvements for more efficient and clinically applicable models.Item Genetic Signatures Predict Social-Cognitive Trajectories in Ultra-High-Risk Psychosis: A 24-month Longitudinal Study(Elsevier BV, 2025-11-06) Doborjeh, Zohreh; Sumich, Alexander; Medvedev, Oleg N; Buchwald, Khan; Doborjeh, Maryam; Singh, Balkaran; Budhraja, Sugam; Merkin, Alexander; Lam, Max; Yee, Jie Yin; Lee, Tih-Shih; Goh, Wilson; Lee, Jimmy; Williams, Margaret; Lai, Edmund M-K; Kasabov, Nikola KBackground Identifying biomarkers that predict social and cognitive outcomes in individuals at ultra-high risk (UHR) for psychosis remains a key challenge in preventive psychiatry. While genetic factors contribute to psychosis vulnerability, specific markers that predict individual trajectories of functional decline or resilience are still unclear. Methods In a 24-month longitudinal study involving UHR (n=45) and healthy control participants (n=54), we investigated for the first time the predictive causal relationship between key immunological genes (FABP5 family and immunoglobulins) and social-cognitive outcomes. Participants completed comprehensive assessments at baseline and four 6-month intervals. We used regression modelling and dynamic Bayesian network analysis to identify predictive relationships between gene expression and behavioral outcomes over time. Results FABP5 family genes (FABP5P1, FABP5P11, FABP5P9) significantly predicted verbal memory (β=0.233, p=0.002); working memory (β=0.225, p=0.004), and social skills (β =-0·190, p<0.029), respectively, at 24 months in the UHR group. Immunoglobulin-related genes showed distinct effects: FCGR2B predicted object recognition ability (β=0.233, p=0.014), while GOT2 inversely predicted planning ability (β=-0.147, p=0.067). Network analysis revealed UHR-specific temporal dependencies absent in controls, with FCGRT emerging as a central node linking genetic markers to changes in processing speed and perceptual closure. Conclusions This study provides the first evidence that FABP5 and immunoglobulin-related genetic markers can predict social-cognitive trajectories in individuals at risk for psychosis. These findings support the use of genetic profiling for early identification and highlight new opportunities for personalized preventive strategies in psychiatry.Item Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture(MDPI AG, 2025-06-09) Garcia-Palencia, Omar; Fernandez, Justin; Shim, Vickie; Kasabov, Nicola Kirilov; Wang, AlanArtificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain’s biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain–computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.Item Machine Learning-Guided High-Definition Transcranial Direct Current Stimulation Prevents Cybersickness(Springer Science and Business Media LLC, 2025-06-10) Yang, AHX; Galán-Augé, C; Kasabov, NK; Cakmak, YOExtended reality (XR) environments, such as simulators, augmented reality, and virtual reality are major techniques in contemporary AI and entertainment systems. Cybersickness (CS) is a motion-sickness experienced by many users of XR. CS causes debilitating nausea, disorientation, and oculomotor issues. Treatment and prevention for motion-sickness typically involves drugs with sedative properties that impair task performance. These drugs are non-specific to CS and counter intuitive for enabling activity within XR. Our paper finds that there are specific spatiotemporal patterns of brain activity in certain functional networks related to CS and offers a method for the analysis of these patterns. The method can predict CS ahead of its onset and most importantly it suggests what intervention to apply in order to prevent CS in individuals. We apply a novel approach to CS prevention by using our previously developed spiking neural network (SNN) method, which can predict CS using electroencephalogram (EEG) pre-VR usage, before applying neuromodulation to disrupt CS-related functional networks in the brain. This approach provides an additional layer of screening before intervention with high-definition transcranial direct current stimulation (HD-tDCS). The study recruited healthy CS susceptible participants (9 male, 10 female, n = 19, 18–36 years old) and used a within-subjects design. EEG (32-channel, 10–10-configuration) was monitored at seated-rest and processed through the SNN for CS prediction. Immediately following a positive prediction, either sham, anodal or cathodal HD-tDCS was applied at the Cz area (5-min, 1.5 mA, 30 s-ramp-up/down) with subsequent 10-min VR immersion to record CS events. Main results: Cathodal stimulation yielded a significantly higher number of successful preventions compared to anodal (*p = 0.01) and sham (***p = 0.00056), achieving a large effect size (> 0.8) with a 47% reduction in CS likelihood. Significance: The treatment was hypothesized to work through disruption of activity at the motor processing and planning regions under Cz. The area appears to be a marker of ongoing CS susceptibility, and also a contributor towards the condition.Item A Generalisability Theory Approach to Quantifying Changes in Psychopathology Among Ultra-High-Risk Individuals for Psychosis(Springer Science and Business Media LLC, 2024-10-04) Doborjeh, Zohreh; N. Medvedev, Oleg; Doborjeh, Maryam; Singh, Balkaran; Sumich, Alexander; Budhraja, Sugam; Goh, Wilson Wen Bin; Lee, Jimmy; Williams, Margaret; M-K Lai, Edmund; Kasabov, NikolaDistinguishing stable and fluctuating psychopathological features in young individuals at Ultra High Risk (UHR) for psychosis is challenging, but critical for building robust, accurate, early clinical detection and prevention capabilities. Over a 24-month period, 159 UHR individuals were assessed using the Positive and Negative Symptom Scale (PANSS). Generalisability Theory was used to validate the PANSS with this population and to investigate stable and fluctuating features, by estimating the reliability and generalisability of three factor (Positive, Negative, and General) and five factor (Positive, Negative, Cognitive, Depression, and Hostility) symptom models. Acceptable reliability and generalisability of scores across occasions and sample population were demonstrated by the total PANSS scale (Gr = 0.85). Fluctuating symptoms (delusions, hallucinatory behaviour, lack of spontaneity, flow in conversation, emotional withdrawal, and somatic concern) showed high variability over time, with 50–68% of the variance explained by individual transient states. In contrast, more stable symptoms included excitement, poor rapport, anxiety, guilt feeling, uncooperativeness, and poor impulse control. The 3-factor model of PANSS and its subscales showed robust reliability and generalisability of their assessment scores across the UHR population and evaluation periods (G = 0.77–0.93), offering a suitable means to assess psychosis risk. Certain subscales within the 5-factor PANSS model showed comparatively lower reliability and generalisability (G = 0.33–0.66). The identified and investigated fluctuating symptoms in UHR individuals are more amendable by means of intervention, which could have significant implications for preventing and addressing psychosis. Prioritising the treatment of fluctuating symptoms could enhance intervention efficacy, offering a sharper focus in clinical trials. At the same time, using more reliable total scale and 3 subscales can contribute to more accurate assessment of enduring psychosis patterns in clinical and experimental settings.Item Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review(MDPI AG, 2024-01-17) Malik, Mishaim; Chong, Benjamin; Fernandez, Justin; Shim, Vickie; Kasabov, Nikola Kirilov; Wang, AlanStroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.Item Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI(MDPI AG, 2023-11-21) Kasabov, Nikola K; Bahrami, Helena; Doborjeh, Maryam; Wang, AlanHumans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.Item Effective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks(Elsevier, 2023-10-28) Maciąg, Piotr S; Bembenik, Robert; Piekarzewicz, Aleksandra; Del Ser, Javier; Lobo, Jesus L; Kasabov, Nikola KIn this article, we introduce a new approach to air pollution prediction using the CEEMDAN time series decomposition method combined with the two-layered ensemble of predictors created based on the stacking and bagging techniques. The proposed ensemble approach is outperforming other selected state-of-the-art models when the bagging ensemble consisting of evolving Spiking Neural Networks (eSNNs) is used in the second layer of the stacking ensemble. In our experiments, we used the PM10 air pollution and weather dataset for Warsaw. As the results of the experiments show, the proposed ensemble can achieve the following error and agreement values over the tested dataset: error RMSE 6.91, MAE 5.14 and MAPE 21%; agreement IA 0.94. In addition, this article provides the computational and space complexity analysis of eSNNs predictors and offers a new encoding method for spiking neural networks that can be effectively applied for values of skewed distributions.Item Filter and Wrapper Stacking Ensemble (FWSE): A Robust Approach for Reliable Biomarker Discovery in High-Dimensional Omics Data(Oxford University Press (OUP), 2023) Budhraja, Sugam; Doborjeh, Maryam; Singh, Balkaran; Tan, Samuel; Doborjeh, Zohreh; Lai, Edmund; Merkin, Alexander; Lee, Jimmy; Goh, Wilson; Kasabov, NikolaSelecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.Item Spiking Neural Networks for Predictive and Explainable Modelling of Multimodal Streaming Data with a Case Study on Financial Time Series and Online News(Springer Science and Business Media LLC, 2023-10-26) AbouHassan, I; Kasabov, NK; Jagtap, V; Kulkarni, PIn a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling.Item Behavioral Outcomes and Neural Network Modeling of a Novel, Putative, Recategorization Sound Therapy(MDPI AG, 2021-04-27) Durai, M; Doborjeh, Z; Sanders, PJ; Vajsakovic, D; Wendt, A; Searchfield, GDThe mechanisms underlying sound’s effect on tinnitus perception are unclear. Tinnitus activity appears to conflict with perceptual expectations of “real” sound, resulting in it being a salient signal. Attention diverted towards tinnitus during the later stages of object processing potentially disrupts high-order auditory streaming, and its uncertain nature results in negative psychological responses. This study investigated the benefits and neurophysiological basis of passive perceptual training and informational counseling to recategorize phantom perception as a more real auditory object. Specifically, it examined underlying psychoacoustic correlates of tinnitus and the neural activities associated with tinnitus auditory streaming and how malleable these are to change with targeted intervention. Eighteen participants (8 females, 10 males, mean age = 61.6 years) completed the study. The study consisted of 2 parts: (1) An acute exposure over 30 min to a sound that matched the person’s tinnitus (Tinnitus Avatar) that was cross-faded to a selected nature sound (Cicadas, Fan, Water Sound/Rain, Birds, Water and Bird). (2) A chronic exposure for 3 months to the same “morphed” sound. A brain-inspired spiking neural network (SNN) architecture was used to model and compare differences between electroencephalography (EEG) patterns recorded prior to morphing sound presentation, during, after (3-month), and post-follow-up. Results showed that the tinnitus avatar generated was a good match to an individual’s tinnitus as rated on likeness scales and was not rated as unpleasant. The five environmental sounds selected for this study were also rated as being appropriate matches to individuals’ tinnitus and largely pleasant to listen to. There was a significant reduction in the Tinnitus Functional Index score and subscales of intrusiveness of the tinnitus signal and ability to concentrate with the tinnitus trial end compared to baseline. There was a significant decrease in how strong the tinnitus signal was rated as well as ratings of how easy it was to ignore the tinnitus signal on severity rating scales. Qualitative analysis found that the environmental sound interacted with the tinnitus in a positive way, but participants did not experience change in severity, however, characteristics of tinnitus, including pitch and uniformity of sound, were reported to change. The results indicate the feasibility of the computational SNN method and preliminary evidence that the sound exposure may change activation of neural tinnitus networks and greater bilateral hemispheric involvement as the sound morphs over time into natural environmental sound; particularly relating to attention and discriminatory judgments (dorsal attention network, precentral gyrus, ventral anterior network). This is the first study that attempts to recategorize tinnitus using passive auditory training to a sound that morphs from resembling the person’s tinnitus to a natural sound. These findings will be used to design future-controlled trials to elucidate whether the approach used differs in effect and mechanism from conventional Broadband Noise (BBN) sound therapy.Item Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks(MDPI AG, 2023-05-06) Wang, Xiang; Yang, Jie; Kasabov, Nikola KIncreasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from videos is vital, especially for small monitoring systems. This paper proposes a two-stream deep learning architecture for video violent activity detection named SpikeConvFlowNet. First, RGB frames and their optical flow data are used as inputs for each stream to extract the spatiotemporal features of videos. After that, the spatiotemporal features from the two streams are concatenated and fed to the classifier for the final decision. Each stream utilizes a supervised neural network consisting of multiple convolutional spiking and pooling layers. Convolutional layers are used to extract high-quality spatial features within frames, and spiking neurons can efficiently extract temporal features across frames by remembering historical information. The spiking neuron-based optical flow can strengthen the capability of extracting critical motion information. This method combines their advantages to enhance the performance and efficiency for recognizing violent actions. The experimental results on public datasets demonstrate that, compared with the latest methods, this approach greatly reduces parameters and achieves higher inference efficiency with limited accuracy loss. It is a potential solution for applications in embedded devices that provide low computing power but require fast processing speeds.Item Machine Learning for Brain MRI Data Harmonisation: A Systematic Review(MDPI AG, 2023-03-23) Wen, Grace; Shim, Vickie; Holdsworth, Samantha Jane; Fernandez, Justin; Qiao, Miao; Kasabov, Nikola; Wang, AlanBACKGROUND: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. OBJECTIVE: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. METHOD: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. RESULTS: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). CONCLUSION: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.Item Investigation of Social and Cognitive Predictors in Non-transition Ultra-high-risk' Individuals for Psychosis Using Spiking Neural Networks(Springer Science and Business Media LLC, 2023-02-15) Doborjeh, Zohreh; Doborjeh, Maryam; Sumich, Alexander; Singh, Balkaran; Merkin, Alexander; Budhraja, Sugam; Goh, Wilson; Lai, Edmund M-K; Williams, Margaret; Tan, Samuel; Lee, Jimmy; Kasabov, NikolaFinding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.Item Machine Learning Methods for the Study of Cybersickness: A Systematic Review(Springer, 2022-10-09) Yang, AHX; Kasabov, N; Cakmak, YOThis systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.Item The Role of Event Related Potentials in Pre-comprehension Processing of Consumers to Marketing Logos(Guilan University of Medical Sciences, and co-published by Negah Institute for Scientific Communication, 2019-01-01) Nazari, MA; Salehi Fadardi, J; Gholami Doborjeh, Z; Amanzadeh Oghaz, T; Saeedi, MT; Yazdi, SAABackground: In human behavior study, by peering directly into the brain and assessing distinct patterns, evoked neurons and neuron spike can be more understandable by taking advantages of accurate brain analysis. Objectives: We investigated the role of Event Related Potentials (ERPs) in pre-comprehension processing of consumers to marketing logos. Materials and Methods: In the framework of an experimental design, twenty-six right-handed volunteers (13 men, 13 women) participated in 2013 in the University of Tabriz. An individual task with a presentation of familiar vs. unfamiliar logos was designed. Stimuli were displayed on a monitor controlled by a PC using the Mitsar® stimulus presentation system PsyTask. Statistical analyses of ERPs data were analyzed by repeated measures ANOVA. Results: Our results showed, when subjects were dealing with familiar logos, higher peak amplitude for the N1 component in right hemisphere of the brain can be observed. These variations on averages of early components of ERPs in occipital lobe can be referred to the pre-perceptual brain activities. Conclusion: Investigating early components of ERP can be utilized further as an effective factor in prediction of the consumers ‘preference particularly in neuromarketing field.Item Classification and segmentation of fMRI Spatio-temporal Brain Data with a NeuCube Evolving Spiking Neural Network Model(IEEE, 2014) Doborjeh, M; Capecci, E; Kasabov, NThe proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period [1]; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube [2]. After the NeuCube is trained with the fMRI samples, the `hidden' spatio- temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results [3] on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.Item Dynamic 3D Clustering of Spatio-temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fMRI and EEG Data(Springer, 2015-02-19) Gholami, M; Kasabov, NThe paper presents a novel clustering method for dynamic Spatio-Temporal Brain Data (STBD) on the case study of functional Magnetic Resonance Image (fMRI). The method is based on NeuCube spiking neural network (SNN) architecture, where the spatio-temporal relationships between STBD streams are learned and simultaneously the clusters are created. The clusters are represented as groups of spiking neurons inside the NeuCube’s spiking neural network cube (SNNc). The centroids of the clusters are predefined by spatial location of the brain data sources used as input variables. We illustrate the proposed clustering method on an fMRI case study STBD recorded during a cognitive task. A comparative analysis of the clusters across different mental activities can reveal new findings about the brain processes under study.