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Machine Learning for BCI
January 21 @ 11:00 am - 12:00 pm
5:00pm CET, 11:00am EST, 8:00am PST, 12:00am CST
Presented by Christian Herff, Maastricht University
Recorded brain activity displays complex non-linear dynamics and is highly participant and even session dependent. Furthermore, the signals often display many non-stationarities. To still make sense of these challenging data, specific consideration for the machine learning approach must be made. In this didactic session, common challenges for machine learning in BCI research will be highlighted and some key considerations of how to address them will be discussed. Briefly, both a regression and a classification problem will be discussed. Quick examples, using both invasive and non-invasive data, will be used to illustrate problems and common fallacies. As in many time series data, specific attention must be paid to the evaluation in BCI research, as the strong autocorrelations of the data can otherwise induce exaggerated results. Finally, some key considerations for the translation from offline results to viable BCI solutions will be presented.