The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems
András Adolf, Csaba Márton Köllőd, Gergely Márton, Ward Fadel, István Ulbert · 2024
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Description
András Adolf and co-authors test how data handling shapes brain-computer interfaces in this 2024 study. Such systems read brain waves so people can control devices by thought. Sorting the signals into the right intent is the hard part. The team used a public data set where people imagined moving their hands or feet.
They tried several cleaning and training steps across five neural network designs. Removing artifacts helped some setups and hurt others. Borrowing training from other tasks lifted accuracy for every network. Lower sound-like frequency bands often carried more useful signal than expected. The lesson is that each pipeline needs tuning for the person and the network at hand.
Catalog record
| CREATOR | András Adolf, Csaba Márton Köllőd, Gergely Márton, Ward Fadel, István Ulbert |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |