Name
Title | ||
|---|---|---|
Mi3dnet: A Compact Cnn For Motor Imagery Eeg Classification With Visualizable Dense Layer Parameters |
Abstract | ||
|---|---|---|
Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176738 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qihang Yang | 1 | 0 | 0.34 |
| Xuan Zhang | 2 | 0 | 0.34 |
| Badong Chen and Nanning Zheng | 3 | 619 | 50.74 |