Name
Abstract | ||
|---|---|---|
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19061423 | SENSORS |
Keywords | Field | DocType |
brain-computer interface (BCI),electroencephalography (EEG),motor-imagery (MI) | Signal processing,Feature selection,Brain–computer interface,Feature extraction,Electronic engineering,Human–computer interaction,Engineering,Electroencephalography,Motor imagery | Journal |
Volume | Issue | ISSN |
19 | 6.0 | 1424-8220 |
Citations | PageRank | References |
8 | 0.67 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natasha Padfield | 1 | 8 | 1.01 |
| Jaime Zabalza | 2 | 151 | 11.51 |
| Huimin Zhao | 3 | 206 | 23.43 |
| Valentin Masero Vargas | 4 | 8 | 0.67 |
| Jinchang Ren | 5 | 1144 | 88.54 |