Iuri,

the spatial filter generates a number of output channels from another number of input channels, each output channel being a linear combination of the input channels.

For example, lets say

*ICj* is the

*j*th input channel,

*OCk* is the

*k*th output channel, and

*Sjk* is the coefficient for the

*j*th input channel and

*k*th output channel in the

*Spatial filter* matrix.

Then the output channels are computed this way :

*OCk* **=** Sum on j ( Sjk * ICj )

Is it clear to you what the spatial filter does ?

For the coefficient orders, all the coefficients for the first output followed by all the coefficients for the second output and so on...

In the example you point out (guess it is the motor imagery BCI), you have :

**Quote:**

Spatial Filter Coefficients: 4 0 -1 0 -1 -1 0 0 -1 0 0 4 0 -1 0 0 -1 -1 0 -1 (20 values)

Number of Output channels: 2

Number of Input channels: 10

Channels List: C3;C4;FC3;FC4;C5;C1;C2;C6;CP3;CP4 (10 channels)

So the output channels are :

OC1

**=** 4 * C3 + 0 * C4 + (-1) * FC3 + 0 * FC4 + (-1) * C5 + (-1) * C1 + 0 * C2 + 0 * C6 + (-1) * CP3 + 0 * CP4

**=** 4 * C3 - FC3 - C5 - C1 - CP3

OC2

**=** 0 * C3 + 4 * C4 + 0 * FC3 + (-1) * FC4 + 0 * C5 + 0 * C1 + (-1) * C2 + (-1) * C6 + 0 * CP3 + (-1) * CP4

**=** 4 * C4 - FC4 - C2 - C6 - CP4

This is basically a Surface Laplacian around C4 and C5.

For your question what would be the correct values to set on it according with my channels list, I would answer depends on what you want to do. In the case of the motor imagery, we wanted to reduce noise and this gave good results.

Hope this helps,

Best regards,

Yann