Browsing by Subject "vector symbolic architecture (VSA)"

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    Hyperdimensional Computing based Classification and Clustering: Applications to Neuropsychiatric Disorders
    (2023-12) Ge, Lulu
    Since its introduction in 1988, hyperdimensional computing (HDC), also referred to as vector symbolic architecture (VSA), has attracted significant attention. Using hypervectors as unique data points, this brain-inspired computational paradigm represents, transforms, and interprets data effectively. So far, the potential of HDC has been demonstrated: comparable performance to traditional machine learning techniques, high noise immunity, massive parallelism, high energy efficiency, fast learning/inference speed, one/few-shot learning ability, etc. In spite of HDC’s wide range of potential applications, relatively few studies have been conducted to demonstrate its applicability. To this end, this dissertation focuses on the application of HDC to neuropsychiatric disorders: (a) seizure detection and prediction, (b) brain graph classification, and (c) transcranial magnetic stimulation (TMS) treatment analysis. We also develop novel clustering algorithms using HDC that are more robust than the existing HDCluster algorithm. In order to detect and predict seizures, intracranial electroencephalography (iEEG) data are analyzed through the use of HDC-based local binary pattern (LBP) and power spectral density (PSD) encoding. Our study examines the effectiveness of utilizing all features as well as a small number of selected features. Our results indicate that HDC can be used for seizure detection, where PSD encoding is superior to LBP encoding. We observe that even three features are efficient in detecting seizures with PSD encoding. However, in order to pave the way for seizure prediction using HDC, more efficient features must be explored. For the classification of brain graphs, data from functional magnetic resonance imaging (fMRI) are analyzed. Brain graphs describe the functional brain connectome under varying brain states, and are generated from the fMRI data collected at rest and during tasks. The brain graph structure is assumed to vary from task to task and from task to no task. Participants are asked to execute emotional and gambling tasks, but no tasks are assigned during resting periods. GrapHD, an HDC-based graph representation, initially developed for object detection, is herein expanded for the application to brain graph classification. Experimental results demonstrate that GrapHD encoding has the capability of classifying the brain graphs for three binary classification problems: emotion vs. gambling, emotion vs. no-task, and gambling vs. no-task. Furthermore, GrapHD requires fewer memory resources as compared to the extant HDC-based encoding approaches. In terms of clustering, HDCluster, an HDC-based clustering algorithm, has been proposed in 2019. Originally designed to mimic the traditional k-means, HDCluster exhibits higher clustering performance across versatile datasets. However, we have identified that the performance of the HDCluster may be significantly influenced by the random seed used to generate the seed hypervectors. To mitigate the impact of this random seed, we propose more robust HDC-based clustering algorithms, designed to outperform HDCluster. Experimental results substantiate that our HDC-based algorithms are more robust and capable of achieving higher clustering performance than the HDCluster. In the analysis of TMS treatment, we conduct two specific tasks. One is to identify the clinical trajectory patterns for patients who suffer from major depressive disorder (MDD) (Clustering). Another is to predict MDD severity using 34 measured cognitive variables (Classification). For clustering, we propose a novel HDC-based algorithm that manipulates HDCluster to determine the number of clusters for a system of clinical trajectories. For classification, we utilize two HDC-based encoding algorithms and examine the impact of using either all features or selected features. Experimental results indicate that our HDC algorithm mirrors the clustering pattern of the classical algorithm. Additionally, the HDC-based classifier effectively predicts the concept of clinical response.