# Programming Tutorial:Implementing another Matlab-based Filter

This tutorial, written by Robert Oostenveld, shows you how to implement a filter based on a beamforming algorithm where the beamformer is updated in real-time, based on the covariance matrix of its input data.

## Contents

## What is Matlab?

“MATLAB® is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran.”

### Advantages of Matlab

- interactive
- simple syntax
- no explicit declaration of variables and functions required
- garbage collection
- standard for neuroscience data analysis
- many toolboxes available
- many algorithms implemented
- many data visualisation tools

### Disadvantages of Matlab

- slower than compiled code
- default double precision
- not open source
- expensive
- language suitable for RAD, not so much for large projects

## Real time processing for BCI

- EEG alpha oscillation @10Hz

- duration ~100 ms
- decision every xx ms?

- Processing as fast as possible

- "real time"

- Deadline requirements vary, e.g.

## Motivation for combining BCI2000 and Matlab

- rapid application development
- try out various algorithms
- offline analysis of data
- port offline analysis to online
- Matlab is fast enough for quite some computations

## Matlab in the BCI2000 pipeline

- BCI2000 filters are pipelined
- each filter is a C++ object, there exists a Matlab filter that translates the C++ interface into Matlab

- constructor -- define states and parameters, start Matlab engine
- Preflight() -- check validity of parameters
- Initialize() -- get parameter values
- StartRun() -- setup computational space
- StopRun() -- cleanup computational space
- while data is streaming
- Process() -- perform actual computation

- destructor -- stop Matlab engine

- correspondingly, there is a Matlab function corresponding to each C++ member function:

- bci_Construct() -- define states and parameters, start Matlab engine
- bci_Preflight() -- check validity of parameters
- bci_Initialize() -- get parameter values
- bci_StartRun() -- setup computational space
- while data is streaming
- bci_Process() -- process a single data block

- bci_StopRun() -- cleanup computational space
- destructor() -- stop Matlab engine

## Filter example: linear classification using a beamformer

Beamforming is a technique to extract source signals from a multi-channel recording. In this hands-on tutorial, we will use an adaptive spatial beamformer to compute a biophysically motivated source projection which is adapted to data covariance.

### Adaptive Spatial Filtering

Region in the brain:

Forward model:

Assumed neural activity:

Model for the projection of the source to the channels (forward model ):

Estimate the strength of activity of the neural tissue at location

An ideal spatial filter should pass activity from a location of interest with unit gain:

while suppressing others

However, this is not always possible, and we compute an optimal spatial filter by minimizing the variance of the filter output (source activity):

Two constraints:

After some algebra:

The adaptive filter is computed from the forward model for a given position , and the covariance matrix of the data.

### Implementation with the BCI2000 Matlab filter

#### bci_StartRun function

function bci_StartRun % shared BCI2000 Parameters and states are global variables. global bci_Parameters bci_States % the following variables are used in the computation, and are needed over multiple iterations global fsample nchans nsamples global sum_covariance sum_count global norm_s norm_ss norm_n global H C w fsample = sscanf(bci_Parameters.SamplingRate{1}, '%fHz'); nchans = sscanf(bci_Parameters.SourceCh{1}, '%d'); nsamples = sscanf(bci_Parameters.SampleBlockSize{1}, '%d'); % these are for the accumulated normalization norm_n = 0; norm_s = 0; norm_ss = 0; % these are for the accumulated data covariance estimate sum_covariance = zeros(nchans, nchans); sum_count = 0; % this is the forward model, which describes how the source projects onto the channels % in real applications the forward model would be computed using a biophysical model % but here the source projects equally strong onto all channels H = ones(nchans,1)/nchans; % these are empty to start with C = zeros(nchans, nchans); w = zeros(1,nchans);

#### bci_Process Function

function out_signal = bci_Process( in_signal ) <...> flt_signal = in_signal; % apply baseline correction for i=1:nchan flt_signal(i,:) = flt_signal(i,:) - mean(flt_signal(i,:)); end % compute the covariance using a running sum sum_covariance = sum_covariance + flt_signal*flt_signal'; sum_count = sum_count + 1; % compute the beamformer spatial filter C = sum_covariance/sum_count; w = inv(H' * inv(C) * H) * H' * inv(C); % apply the beamformer spatial filter to the data out_signal = w * flt_signal; % compute the total power in the signal for the present block out_signal = sqrt(sum(out_signal.^2)); % we could in principle stop here, but normalization of the control signal is required as well % the normalization could also be done with standard BCI2000 filter % if the signal is all zero, then the inverse covariance cannot be computed % which results in a filter output that is not a number (nan) if ~isnan(out_signal) % compute the running sum of the beamformer power for i=1:numel(out_signal) norm_n = norm_n + 1; norm_s = norm_s + out_signal(i); norm_ss = norm_ss + out_signal(i)^2; end end % compute the normalized output norm_avg = norm_s / norm_n; norm_std = sqrt(norm_ss/norm_n - norm_s^2/norm_n^2); out_signal = (out_signal - norm_avg)/norm_std;

### Beamformer source reconstruction

- adaptive spatial filter
- implementation in few lines of Matlab code
- recursive updating of covariance
- output used as control signal

## Conclusions

- Small-batch processing in Matlab
- Incremental algorithms
- Within the pipeline
- Usefull for Rapid Application Development
- Fast for Research & Development
- Fast enough (?) for online applications
- once proven, port algorithm to C++

## See also

Programming Tutorial:Implementing a Matlab-based Filter, Programming Reference:MatlabFilter, User Reference:MatlabFilter, Contributions:FieldTripBuffer