### Wolpaw Cursor Control Regression Weights

Posted:

**22 Jan 2016, 15:55**Hello,

I am currently working on my thesis about motor imagery EEG-based BCI's, and I ran into a bit of a problem trying to understand how the Wadsworth BCI derives the weights for the control signal in cursor control applications.

So in "Emulation of computer mouse control with a noninvasive brain computer interface" they discuss using a linear least-squares criteria and say that feature weights are calculated as b = (X'X)^(-1)X'Y, where X is an m by n matrix formed from the n observations of m predictor variables (i.e., EEG amplitudes at specific frequencies and locations) and Y is the vector of n values (i.e., target predictions) to be predicted.

The actual linear equation controlling (vertical) cursor movement is: delta V = b_V(S_V-a_V) where S_V is just the selected features weighted by the weights obtained from the Least-squares regression, b_V is the gain, and a_V is the mean of the vertical control signal for the user's previous performance.

So my question is whether or not X and Y are augmented with ones to generate an intercept for the regression equation and that value is just tossed out in computing S_V, or if the X and Y are not augmented in which case they force the regression line to pass through the origin of feature space. It just doesn't mathematically make a lot of sense to me if you force the intercept to be 0 when doing regression because your line will not fit the data as well. However, it doesn't make a whole lot of sense to completely throw out the intercept either.

Does anyone have any insight into what I'm not understanding about all of this?

Thank you,

James

I am currently working on my thesis about motor imagery EEG-based BCI's, and I ran into a bit of a problem trying to understand how the Wadsworth BCI derives the weights for the control signal in cursor control applications.

So in "Emulation of computer mouse control with a noninvasive brain computer interface" they discuss using a linear least-squares criteria and say that feature weights are calculated as b = (X'X)^(-1)X'Y, where X is an m by n matrix formed from the n observations of m predictor variables (i.e., EEG amplitudes at specific frequencies and locations) and Y is the vector of n values (i.e., target predictions) to be predicted.

The actual linear equation controlling (vertical) cursor movement is: delta V = b_V(S_V-a_V) where S_V is just the selected features weighted by the weights obtained from the Least-squares regression, b_V is the gain, and a_V is the mean of the vertical control signal for the user's previous performance.

So my question is whether or not X and Y are augmented with ones to generate an intercept for the regression equation and that value is just tossed out in computing S_V, or if the X and Y are not augmented in which case they force the regression line to pass through the origin of feature space. It just doesn't mathematically make a lot of sense to me if you force the intercept to be 0 when doing regression because your line will not fit the data as well. However, it doesn't make a whole lot of sense to completely throw out the intercept either.

Does anyone have any insight into what I'm not understanding about all of this?

Thank you,

James