title: System for Electroencephalographic signals classification using machine learning
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Ð¡Ð¸ÑÑÐµÐ¼Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Creators: name: ÐÐ»Ñ-ÐÐ½Ð¸Ð½Ð°Ð²Ð¸, ÐÑÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Parkinsonâs disease
EEG signial
classification
Ð±Ð¾Ð»ÐµÐ·Ð½Ñ ÐÐ°ÑÐºÐ¸Ð½ÑÐ¾Ð½Ð°
Ð­Ð­Ð-ÑÐ¸Ð³Ð½Ð°Ð»Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ

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description: These studies provide an outline of the category of electroencephalography (EEG) stimulation, a procedure that entails studying electrical interest in the brain to identify patterns associated with specific intellectual states or activities. Improving the overall performance and accuracy of EEG signal class systems is crucial for reliable and valid interpretation of brain stimulations, leading to more accurate diagnoses and treatment plans for neurological conditions. These studies are devoted to developing and comparing superior neural network models for the Parkinsons disease population using biomedical acoustic measurements. A dataset of 42 people with early-stage Parkinsons disease was used. Four neural network architectures were used: simple neural network, Transformer Neural Networks (TNN), Recurrent neural network (RNN), and also Convolutional neural network (CNN). Rigorous data preprocessing was performed, and the dataset was partitioned. On training and test sets. The RNN model outperformed the rest of the models with an accuracy rate of 97.43% and an execution time of 7 milliseconds. An RNN model was also developed and trained on NVidia Jetson Nano software with an accuracy rate of 94.87% and an execution time of 23.9 milliseconds. Various software and hardware techniques were also reviewed to improve performance and accuracy. Analysis of the results highlights the importance of choosing the optimal approach by considering factors such as computational efficiency, real-time processing capabilities, and accuracy rates.