| 1. |
- Belic, Jovana, 1987-, et al.
(författare)
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Behavior Discrimination Using a Discrete Wavelet Based Approach for Feature Extraction on Local Field Potentials in the Cortex and Striatum
- 2015
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Ingår i: 7th International IEEE/EMBS Conference on Neural Engineering (NER). - : IEEE conference proceedings. - 9781467363891 ; , s. 964-967
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Konferensbidrag (refereegranskat)abstract
- Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.
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| 2. |
- Zhuang, C., et al.
(författare)
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A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation
- 2015
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Ingår i: International IEEE/EMBS Conference on Neural Engineering, NER. - : IEEE Computer Society. - 9781467363891 ; , s. 816-819
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Konferensbidrag (refereegranskat)abstract
- Recent experiments have suggested that the central nervous system (CNS) makes use of muscle synergies as a neural strategy to simplify the control of a variety of movements by using a single pattern of neural command signal. This nature of muscle coordination could have great significance in the treatment and rehabilitation of upper limb impairments for hemiparetic patients post stroke. The use of neuromuscular electrical stimulation (NMES) for neural prosthetics or therapeutic applications has been demonstrated as a promising clinical intervention for stroke patients to recover motor function of the upper extremity. However, the existing NMES systems do not provide control methods for the patient to achieve an individualized and functional rehabilitation training. In this research work, muscle synergies from the flexionextension elbow antagonistic muscles were studied. Using motion information and EMG signals, muscle synergies were extracted using non-negative matrix factorization (NMF) method. Reconstructed signals obtained from the muscle synergies were then applied to the virtual arm (VA) model to test a synergy based NMES strategy. Results show close resemblance to the original elbow trajectory of normal movements and thus the feasibility to control movements in stroke patients for rehabilitation.
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