Name
Title | ||
|---|---|---|
Classification Of Motor Imagery Electrocorticogram Signals For Brain-Computer Interface |
Abstract | ||
|---|---|---|
In recent several decades, brain-computer interface (BCI) technology continually yield fruitful results. The electrocorticogram (ECoG) has attracted considerable interest because of its advantages of higher signal-to-noise ratio and greater long-term stability than electroencephalography (EEG) signals. We present an optimal scheme of ECoG signals for motor imagery (MI) classification. The time-frequency features are first extracted by the modified S-transform (MST) algorithm, and then a classifier is trained by using the support vector machine (SVM). In addition, channel selection is performed to reduce the computational complexity of MI-based BCI scheme. This method was tested on BCI Competition III dataset I. The MST coupled with the SVM can obtain the satisfactory classification of 95%. Channel selection can greatly reduce the computational burden of classification and enable this scheme to classify MI tasks in real time. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ner.2019.8716963 | 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) |
Field | DocType | ISSN |
Computer vision,Computer science,Brain–computer interface,Artificial intelligence,Motor imagery | Conference | 1948-3546 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenfeng Zheng | 1 | 6 | 4.53 |
| Fangzhou Xu | 2 | 10 | 5.87 |
| Minglei Shu | 3 | 20 | 9.57 |
| Yingchun Zhang | 4 | 1 | 4.07 |
| Qi Yuan | 5 | 85 | 9.61 |
| Jian Lian | 6 | 37 | 11.49 |
| Yuanjie Zheng | 7 | 671 | 55.01 |