Linear Classifier Matrix ambiguous
Posted: 12 Nov 2008, 07:20
Hi,
I did an mu offline analysis of 1D control experiment, I have 3 conditions Left, Right, Rest, and I am recording from C3,C4,Cz. The analysis showed a strong component around 9Hz, and clear difference between Rest-Left, Rest-Right, however there is no clear difference between Left-Right. So I decided to use Rest-Right for example to perform another 1D control experiment. My question is how can I translate this into parameters of the Linear Classifier matrix, since they are two separate events. I used to use two input channels C3,C4 and one output channel 1 , freq is set to 10Hz, and the weights for C3 is 1 and for C4 is -1.
I still found the linear matrix ambiguous, and the way the control signal is generated as well. I know the control signal will be the summation of the input signal features at the specified frequency times the corresponding weights in the matrix, however, this is computed during the current event, is that right?
this is how my Linear Classifier matrix, I use an identity matrix for spatial filtering
================================
Filtering:LinearClassifier matrix Classifier= 2 { input%20channel input%20element%20(bin) output%20channel weight } 1 10Hz 1 -1 2 10Hz 1 1 // Linear classification matrix in sparse representation
=================================
your help is appreciated,
Omar,
I did an mu offline analysis of 1D control experiment, I have 3 conditions Left, Right, Rest, and I am recording from C3,C4,Cz. The analysis showed a strong component around 9Hz, and clear difference between Rest-Left, Rest-Right, however there is no clear difference between Left-Right. So I decided to use Rest-Right for example to perform another 1D control experiment. My question is how can I translate this into parameters of the Linear Classifier matrix, since they are two separate events. I used to use two input channels C3,C4 and one output channel 1 , freq is set to 10Hz, and the weights for C3 is 1 and for C4 is -1.
I still found the linear matrix ambiguous, and the way the control signal is generated as well. I know the control signal will be the summation of the input signal features at the specified frequency times the corresponding weights in the matrix, however, this is computed during the current event, is that right?
this is how my Linear Classifier matrix, I use an identity matrix for spatial filtering
================================
Filtering:LinearClassifier matrix Classifier= 2 { input%20channel input%20element%20(bin) output%20channel weight } 1 10Hz 1 -1 2 10Hz 1 1 // Linear classification matrix in sparse representation
=================================
your help is appreciated,
Omar,