Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network /

By Nazir, Abida . . 44p. , Reaching and Grasping is most signi cant component of human life.Translation of EEG in the form of upper limb movement is of great importance for realization of natural neuroprosthesis control and restoration of hand movements of patients with motor disorders. Patients su ering from spinal cord injury (SCI)problems have lost most of voluntary motor control functions. Such type of loss can be cured using movement related cortical potentials (MRCPS) analysis. Brain computer interface with limb neuro-prosthesis is considered as a solution to such problems. This study anlyzes EEG signals in relation with natural reach and grasp actions. EEG signals have movement related cortical potentials (MRCPS) which can be used to decode upper limb movements. This experiment was performed in Graz University of Technology Austria and they o ered free access dataset for further exploration.Total 45 subjects were involved in this study, 15 subjects with every type of electrode:gel,water and dry performed the experiment. All subjects accomplished self-initiated 80 reach and grasp actions toward a spoon within the jar (lateral grasp) and toward an empty glass (palmar grasp).EEG signals are recorded using three types of electrodes: water based, Gel based and Dry electrodes. In this study signals are classi ed using Deep learning technique i.e Convulotional Neural Networks. For analysis, EEG signals were preprocessed using various lteration techniques. After ltration data is fed into classi er for classi cation of signals. Data is divided into test set and training set. Grand average peak accuracy calculated on unseen test data resulted in 54.2% classi cation accuracy i.e Gel based accuracy approached 56.8.4%, water based 52.7% and dry based 51.8%. 30cm.

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High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /

By Shabbir, Nida . . 94p. , Low back pain (LBP) is one of the most prevalent musculoskeletal disorders worldwide, with diagnosis often relying on subjective evaluation rather than objective physiological measures. This thesis introduces a data-driven framework for quantitative assessment of LBP using high-density surface electromyography (HD-sEMG). As a non-invasive technique, HD-sEMG provides insight into spinal neuromuscular behavior, enabling spatial and temporal characterization of muscle activity patterns associated with dysfunction. The study recruited 39 participants, divided into three groups that are healthy, sub-clinical, and LBP based on chiropractic evaluation. In the first study, machine learning classifiers including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were trained on time and frequencydomain features to discriminate between groups. The SVM model achieved the highest accuracy, effectively distinguishing subtle neuromuscular differences between healthy and dysfunctional subjects. In the second study, a regression-based framework was developed to predict vertebral joint dysfunction scores (C1-Sacral) derived from chiropractic assessment. ANN and Convolutional Neural Network (CNN) models were trained under a CORAL (Consistent Rank Logits) ordinal regression framework, preserving the ordinal nature of dysfunction severity. The ANN model demonstrated superior predictive performance, capturing non-linear relationships between HD-sEMG activity and graded dysfunction levels. Overall, this research bridges the gap between clinical assessment and computational diagnostics, showing that HD-sEMG signatures can objectively quantify spinal dysfunction and support data-driven LBP diagnosis. The proposed framework establishes a foundation for personalized rehabilitation, automated dysfunction mapping, and AI-assisted musculoskeletal diagnostics, advancing the integration of biomedical signal processing with clinical neurophysiology. 30cm.

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Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network /

High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /