title: ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð°ÑÑÐµÑÐ½Ð¾Ð² Ð²Ð¾Ð¾Ð±ÑÐ°Ð¶Ð°ÐµÐ¼ÑÑ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ð²ÐµÐ¹Ð²Ð»ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Motor imaginary classification of electroencephalographic patterns using deep convolutional networks and wavelet transforms
Creators: name: ÐÑÑÑÐ¸Ð½, ÐÐ°ÑÐ°Ñ
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: Ð¼Ð¾Ð·Ð³Ð¾-ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÐµ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÑ
Ð¼Ð¾ÑÐ¾ÑÐ½ÑÐµ Ð²Ð¾Ð¾Ð±ÑÐ°Ð¶Ð°ÐµÐ¼ÑÐµ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð²Ð¾Ð¾Ð±ÑÐ°Ð¶Ð°ÐµÐ¼ÑÑ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ð¹
Ð­Ð­Ð ÑÐ¸Ð³Ð½Ð°Ð»
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð²ÐµÐ¹Ð²Ð»ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ñ
ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
brain-computer interfaces
motor imaginations
motor imagery classification
EEG signal
machine learning
wavelet transforms
convolution neural networks

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ Ð¼Ð¾Ð·Ð³-ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÑ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ¾Ð², Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ. ÐÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð¾ÑÐ¾ÑÐ½ÑÑ Ð²Ð¾Ð¾Ð±ÑÐ°Ð¶Ð°ÐµÐ¼ÑÑ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ð¹, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð²ÐµÐ¹Ð²Ð»ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸ÑÑ Ð¸ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ. Ð Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ñ Ð¿Ð°ÑÐ°Ð´Ð¸Ð³Ð¼Ð¾Ð¹ HaLT Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Â«Large EEG DatasetÂ» Ñ 6 ÐºÐ»Ð°ÑÑÐ°Ð¼Ð¸ Ð²Ð¾Ð¾Ð±ÑÐ°Ð¶Ð°ÐµÐ¼ÑÑ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸Ð¹. ÐÑÐ¾Ð³Ð¾Ð²Ð°Ñ Ð´Ð¾Ð»Ñ Ð²ÐµÑÐ½ÑÑ Ð¾ÑÐ²ÐµÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼ÐµÐ¶Ð´Ñ ÑÑÐ±ÑÐµÐºÑÐ°Ð¼Ð¸ â 0.71, Ð° Ð¿ÑÐ¸ Ð²Ð½ÑÑÑÐ¸ ÑÑÐ±ÑÐµÐºÑÐ½Ð¾Ð¹ â 0.74 (ÑÑÐµÐ´Ð½ÑÑ) Ð¸ 0.81 (Ð¼ÐµÐ´Ð¸Ð°Ð½Ð½Ð°Ñ). ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾, Ð² ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð° Ð²ÐµÐ¹Ð²Ð»ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð­Ð­Ð Ð³Ð¾Ð»Ð¾Ð²Ð½Ð¾Ð³Ð¾ Ð¼Ð¾Ð·Ð³Ð° Ð¸ Ð¸Ñ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼. ÐÑÐ» Ð¾Ð¿Ð¸ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð²ÐµÐ¹Ð²Ð»ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¹, Ð° ÑÐ°ÐºÐ¶Ðµ Ð½Ð°Ð¿Ð¸ÑÐ°Ð½ ÐºÐ¾Ð´ Ð´Ð»Ñ Ð¸Ñ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð½Ð° Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐ°Ñ.
description: This paper provides an overview of brain-computer interfaces and their classification. An algorithm for classifying motor imaginary movements based on wavelet transformations and convolutional neural networks has been developed. The work was performed with the HaLT paradigm of the "Large EEG Dataset" with 6 classes of imaginary movements. The final accuracy metric in the cross-subject classification is 0.71, and in the case of within-subject classification â 0.74 (average) and 0.81 (median). In addition, the work investigated some properties of wavelet transformations, as well as brain EEG signals and their spectrograms. An algorithm for wavelet transformations was described, and code was written for their parallel calculation on GPUs.