Name
Title | ||
|---|---|---|
A Motor Imagery Eeg Signal Classification Algorithm Based On Recurrence Plot Convolution Neural Network |
Abstract | ||
|---|---|---|
With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields. ? 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patrec.2021.03.023 | PATTERN RECOGNITION LETTERS |
Keywords | DocType | Volume |
EEG signal, Recurrence plot, Convolution neural network, Classification, Motor imagery | Journal | 146 |
ISSN | Citations | PageRank |
0167-8655 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianjia Meng | 1 | 2 | 3.06 |
| Shi Qiu | 2 | 250 | 29.03 |
| Shaohua Wan | 3 | 382 | 48.34 |
| Keyang Cheng | 4 | 0 | 0.68 |
| Lei Cui | 5 | 0 | 0.34 |