A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Data Space Adaptation for Multiclass Motor Imagery-based BCI
2018
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. This paper proposes a supervised multiclass data space adaptation technique (MDSA) to transform the test data using a linear transformation such that the distribution difference between the multiclass train and test data is minimized. The results of
doi:10.1109/embc.2018.8512643
pmid:30440793
fatcat:q55wuay4xzgwtnxsviqqtbufli