ACTIVITY COMMAND ENCODING OF CEREBRAL CORTEX M1-EVOKED POTENTIALS OF THE SPRAGUE DAWLEY RAT USING TIME DELAY NEURAL NETWORKS
| Autor: | Rong-Chin Lo, Ren-Guey Lee, Yuan-Hao Chen, Tung-Tai Kuo, Shang-Hsien Cai |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
010401 analytical chemistry 0206 medical engineering Biomedical Engineering Biophysics Bioengineering 02 engineering and technology Biology 020601 biomedical engineering 01 natural sciences 0104 chemical sciences Sprague dawley medicine.anatomical_structure Cerebral cortex Encoding (memory) medicine Neuroscience |
| Zdroj: | Biomedical Engineering: Applications, Basis and Communications. 32:2050034 |
| ISSN: | 1793-7132 1016-2372 |
| DOI: | 10.4015/s1016237220500349 |
| Popis: | Understanding the neurons that transmit messages in the brain while we thinking, feeling, or acting is critical for research on the causes of neurological disease and treatment strategies. This research focuses on the primary motor cortex M1 region, which is involved in human motor function as an activity command center. Understanding this region can help us to determine the mechanism of movement control by the brain, with applicability to other activity mechanisms. A time delay neural network (TDNN) is a suitable model for studying brain signals. TDNN can analyze comprehensive information for a period of successive signals, which is similar to the transmission mechanism of the M1 region. Therefore, this study used a TDNN to build a three-stage encoding system corresponding to the signal type, type arrangement, and time sequence of the brainwave signal from the M1 region and the encoded results were defined as codes, symbols, and commands, respectively. This study aimed to understand the relationship between movement and the M1 region by decoding the signal when the rat undertakes an action. First, we recorded the M1 signal from three rat action types (walk, stand up, and shift head) and performed signal processing. This included using a nonlinear energy operator to find the response points of each action signal. The signals were separated into several sections according to the response time points and independent component analysis was then used to extract the features of the signal (the signal of interest). Finally, we found 16 representative sample signals through a dynamic dimension increasing algorithm to train a three-stage TDNN. We then input the remaining feature signals of interest into the three-stage TDNN for encoding and classification. The results showed an accuracy rate for the three actions of 51.4%, 80.0%, and 54.3%, which means that it is feasible to explain the brain signal of M1 from the free-moving animal using a three-stage TDNN encoding model. |
| Databáze: | OpenAIRE |
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