The use of EEG-Neurofeedback in rehabilitation for speech disorders in patients after stroke

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Introduction: Ischemic stroke is one of the leading causes of cognitive disability, including speech disorders. EEG-Neurofeedback is one of the newest method in treatment of people after stroke. Material and methods: Altogether 58 patients after ischemic stroke were qualified for the study, divided into groups of experimental (n = 40) and control (n = 18). Neuropsychological therapy were used as rehabilitation method for all patients. The study group patients received additionally EEG-NFB therapy, conducted in a cycle of 15 sessions. Verbal fluency test was used to assessed therapy effectiveness in speech disorders rehabilitation. Results: The test results confirmed the thesis that the EEG-NFB training and neuropsychological therapy conducted together improve verbal fluency in adults after stroke and that they are more effective in this subject than the neuropsychological therapy used separately. It was also confirmed that the initial level of verbal fluency in stroke survivors has no significant influence on the effectiveness of EEG-NFB therapy. Conclusions: In regard to cognitive rehabilitation, EEG-NFB appears to hold great promise for the rehabilitation of speech in stroke patients.

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... In the same year, Mroczkowska confirmed using EEG-Neurofeedback (EEG-NFB) to rehabilitate speech disorders in patients after stroke. Their experiment proves that EEG-NFB can be more effective in helping stroke survivors regain verbal fluency than traditional neurophysiological therapy [41]. ...
... All patients received neuropsychological therapy, while the experimental group received additional EEG-NFB therapy. After testing the verbal fluency, the result shows that the combination of EEG-NFB therapy with neuropsychological therapy can improve stroke patients' verbal fluency [41]. • Nafea created a brainwave-controlled system that allows the user to switch on and off home applications using blinking and attention levels. ...
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This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.
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