# Difference between revisions of "User Reference:Filters"

Filters are the most important building blocks of a BCI2000 system. This document describes the filter concept for users who will configure BCI2000 for their own experiments, and provides links to the documentation pages of individual filters, describing each filter's purpose and parameterization. On this page, only core components of BCI2000 are described; for contributed filters, see Contributions:Contents.

## Data Processing as a Pipe

Each of the three BCI2000 core modules contains a chain of filters, i.e. a sequence of filters forming a pipe where, basically, brain signal data enter on one side, and a processed version of these data leaves on the other side.

The notion of a pipe implies that, for each portion of data entering on the input side, there will be exactly one portion of output data on the output side. This is analogous to a water pipe: unlike a water stream (a brook, or a river), it is impossible to insert or remove water from inside the pipe without breaking it. Similarly, albeit signal portions may change their shape on their way through the pipe (filter chain), it is impossible to insert or remove any of them. Thus, each data portion acquired by the data acquisition module will run through the entire BCI2000 system, being processed by a sequence of filters.

This page is mostly about the way filters are used online in BCI2000 core modules. Note however that the same filter code can be accessed in order to recreate a processing chain offline, either from the system command-line or from within Matlab.

## Configuration Overview

In principle, these filters may be placed in any order by the writer (programmer) of a BCI2000 module, and it is technically easy to change the number, and order, of filters. However, not all combinations or orderings of filters make sense.

The flowchart shows modules and filters for BCI2000's configurations included in the core distribution:

Flowchart of the Data moving through the Filter Pipeline

## Data Acquisition Module

In the data acquisition (source) module, the DataIOFilter manages data acquisition and storage in a general manner. Actual acquisition of data is provided by ADC filters, actual writing into data files is done by FileWriter filters representing various Data Formats. In addition to managing the operation of ADC and FileWriter filters, the DataIOFilter handles signal calibration into physical units (typically, $\mu V$), and visualization of the source signal.

Filter documentation for source modules is available for

## Signal Processing Module

In the signal processing module, brain signals are filtered spatially and temporally, resulting in a set of extracted features. In the Classifier, these features are used to differentiate amongst a small number of mental states (classes). Finally, the Normalizer adjusts the Classifier's output to zero mean and unit variance.

Filter documentation for signal processing filters is available for

## Application Module

Basically, the application module contains a single filter that handles trial sequencing and brain signal feedback. In the standard configuration, this task filter is surrounded by ConnectorInput and ConnectorOutput filters. These connector filters allow for exchanging data with external software over a UDP based socket protocol.

Filter documentation of application module filters is available for