4.6 Article

A Novel Active Rehabilitation Model for Stroke Patients Using Electroencephalography Signals and Deep Learning Technology

期刊

FRONTIERS IN NEUROSCIENCE
卷 15, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2021.780147

关键词

stroke; rehabilitation model; deep learning technology; deep neural network model; EEG recognition

资金

  1. Scientific Research Program of Education Department of Hubei Province, China [D20184101]
  2. Higher Education Reform Project of Hubei Province, China [201707]
  3. East Lake Scholar of Wuhan Sports University Fund, China
  4. Hubei Provincial University Specialty subject group construction Special fund, China

向作者/读者索取更多资源

The main clinical manifestations of stroke include motor, language, sensory, and mental disorders, with the possibility of resulting in sequelae such as numbness, facial paralysis, and central paralysis if not effectively treated. Effective rehabilitation training is crucial for stroke patients to reduce the disease and restore motor function, especially considering the prevalence of upper limb paralysis among those affected.
The main clinical manifestations of stroke are motor, language, sensory, and mental disorders. After treatment, in addition to being conscious, other symptoms will still remain in varying degrees. This is the sequelae of stroke, including numbness, facial paralysis, central paralysis, and central paralysis. If the sequelae of stroke are not treated effectively, they can easily develop into permanent sequelae. Most of the affected people have sequelae, and most of them have symptoms of upper limb paralysis. Therefore, it is of great significance to study how to carry out effective rehabilitation training for stroke patients to reduce the disease and even restore their motor function. Based on this background, this research aims to use deep learning technology to design a stroke rehabilitation model based on electroencephalography (EEG) signals. First, the patient's EEG signal will be preprocessed. Then, an improved deep neural network model (IDNN) is used to get the EEG classification results. The traditional DNN model construction process is simple and suitable for scenarios where there is no special requirement for the data format, but the generalization of a single DNN model is usually poor. Large margin support vector machine (LM_SVM) is an extension method of support vector machine (SVM), suitable for any occasion. By optimizing the edge distribution, better generalization performance can be obtained. Taking into account the advantages of DNN and LM_SVM and the high aliasing characteristics of stroke data, an improved DNN model is proposed. Finally, based on the EEG recognition result of the model, the rehabilitation equipment is controlled to assist the patient in rehabilitation treatment. The experimental results verify the superiority of the EEG classification model used, and further prove that this research has good practical value.

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