ESP-ThinkGear - EEG Signal Processing for NeuroSky ThinkGear Devices

This library provides the NeuroSky MindWave ThinkGear connector implementation of the ESP library base classes. It is designed to be fast and efficient, using high performance Java libraries:

Release 2.1, 25-05-14

Added Lab and Connection abstractions (explained below)
Implemented multi channel support

Release 2.0, 29-04-14

Added RxJava to the project
Added ability to tune sample buffer size to more closely represent 1 second's worth of data
Utilizing Disruptor in raw sample buffer
Sample rate and sample size now mutable
Sample rates above 1kHz allowed
Reasonable results sampling into the 50kHz range

Design Goals

ESP is designed to provide Java programs access to EEG device signals and provide the ability to process the signals for the purposes of the program. The ESP library itself is not an implementation, however there are several implementations for specific EEG devices:

ESP provides a common structure for device specific implementations. This allows programs using the ESP libraries to cater for multiple devices from a single codebase.

Primary Design Specifics

The MultiConnectSocket Interface

This interface defines the methods that must be implemented for a class which provides access to EEG hardware. An abstract superclass exists for ease of implementation.

The RawEspConnection Interface

RawEspConnection extends the MultiConnectSocket interface. Implementations are intended to encapsulate and control a MultiConnectSocket instance through the implementation of the common methods, as well as provide the current seconds' worth of samples on demand for direct use or further processing. Signal processing methods exist to provide ease of processing the signal. An abstract superclass exists for ease of implementation.

The Lab Interface

While the ESP library provides DSP processing classes, how the signal is processed is left to programs using the library. Lab implementations provide the ability to obtain the current seconds' worth of samples from the encapsulated RawEspConnection, process the signal and notify the program when a sample is ready for use.

A default implementation exists and an abstract superclass provides ease of custom lab implementation.

Usage

Direct MultiConnectSocket Usage

This is the lowest level use case. Programs using ESP library implementations in this manner will receive device-specific signals as they occur. Each implementation of the ESP library's MultiConnectSocket has a device-specific listener mechanism to obtain the signal. Pseudo code for usage appears as so:

    MultiConnectionThinkGearSocket socket = new MultiConnectionThinkGearSocket();

    // optional, common to all MultiConnectSocket implementations:
    // add a listener to receive connect/disconnect events
    socket.addConnectionEventListener(new ConnectionEventListener() {
        public void connectionEventPerformed(ConnectionEvent e) {
            doSomethingWith(e);
        }
    });

    socket.addListener(new ThinkGearEventListener() {
        public void thinkGearEventPerformed(AbstractMultiConnectionEvent<EventType> e) {
            doSomethingWith(e);
        }
    });

    socket.start();

Direct MultiConnectSocket Usage - Remote Connection

This functionality is from where the MultiConnectSocket derives its name. Device specific implementations provide the ability to open a socket on a configurable port. Such sockets facilitate a subscribe and publish of device signals. This allows separate processes and machines to receive the device signals for their own purposes - signal recording, secondary processing, display etc.

    // on the host machine or process:
    // port is set via the system property 'socket.broadcaster.port', default '12345'
    MultiConnectionThinkGearSocket socket = new MultiConnectionThinkGearSocket(true);

    // on the remote process/machine:
    // port is set via the system property 'socket.broadcaster.port', default '12345'
    ThinkGearSocketConnector connector = new ThinkGearSocketConnector("host machine name or IP address");

    connector.addListener(new ThinkGearEventListener() {
        public void thinkGearEventPerformed(AbstractMultiConnectionEvent<EventType> e) {
            doSomethingWith(e);
        }
    });

    connector.connect();
    connector.subscribe(....);

    // and back on the host machine or process:
    socket.start();

RawEspConnection Usage

RawEspConnection implementations aggregate the current second's worth of data, insulating the program from device specific listeners. The samples are intended to be queried periodically in a separate scheduled task or thread. The period of querying is left to the program and is independent of sample rate.

    ThinkGearConnection connection = new ThinkGearConnection();

    // optional, common to all RawEspConnection implementations:
    // add a listener to receive connect/disconnect events
    connection.addConnectionEventListener(new ConnectionEventListener() {
        public void connectionEventPerformed(ConnectionEvent e) {
            doSomethingWith(e);
        }
    });

    connection.start();

    // in a separate scheduled periodic task..
    int channel = 1; // channel of interest 
    dealWithCurrentSecondOfSamples(connection.getCurrentFor(channel));

Lab Usage

Lab implementations provide the ability to process the current seconds' worth of data from the RawEspConnection and notify any interested parties of its completion. Triggering of signal processing is intended to be executed periodically in a separate scheduled task or thread. The period of triggering is left to the program and is independent of sample rate.

    ThinkGearConnection connection = new ThinkGearConnection();

    // optional, common to all RawEspConnection implementations:
    // add a listener to receive connect/disconnect events
    connection.addConnectionEventListener(new ConnectionEventListener() {
        public void connectionEventPerformed(ConnectionEvent e) {
            doSomethingWith(e);
        }
    });

    Lab lab = connection.getDefaultLab();

    int numBands = 40; // first 40 bands
    lab.setNumBands(numBands); // must be set

    int channel = 1; // channel of interest for multichannels
    lab.setChannel(channel); // required if > 1 channel

    lab.addSignalProcessedListener(new SignalProcessedListener() {
        public void signalProcessed(double[] processed) {
            doSomethingWith(processed);
        }
    });

    // other lab values set as appropriate for processing the signal

    connection.start();

    // in a separate scheduled periodic task..
    lab.triggerProcessing();

Maven Dependency

   <dependency>
       <groupId>com.github.mrstampy</groupId>
       <artifactId>esp-thinkgear</artifactId>
       <version>2.1</version>
   </dependency>

Usage of the library is straight-forward. One only needs to instantiate the MultiConnectionThinkGearSocket implementation, add a listener, start the socket and deal with the events as they occur.

Multiple Connections

As implied by the name of the class, the MultiConnectionThinkGearSocket is capable of not only processing ThinkGear messages for a single application but also of broadcasting the messages to separate applications, even on separate machines and devices! This functionality is disabled by default. To enable it either set the System property 'broadcast.messages' to true or instantiate the MultiConnectionThinkGearSocket using one of its other constructors:

MultiConnectionThinkGearSocket socket = new MultiConnectionThinkGearSocket("ThinkGear socket hostname", true);
...
socket.start();

...and then in another application:

ThinkGearSocketConnector connector = new ThinkGearSocketConnector("Hostname running MultiConnectionThinkGearSocket");

connector.addThinkGearEventListener(new ThinkGearEventListenerAdapter() {
    // overriding methods as appropriate
});

connector.connect();

//after successful connection...
connector.subscribe(EventType...);

Raw Signal Processing (as of version 1.3)

In the development of the ESP-Nia implementation the base classes for raw signal processing were developed. Multi connection socket implementations contain a raw data buffer which contains the current second's worth of data (for the OCZ Nia this translates to 3906 data points, for the ThinkGear devices this translates to 512 data points).

Additional classes have been added to assist with raw signal processing. The examples shown will work with the NeuroSky raw output however these classes can be used for any DSP work.

Additional functionality is described in the JavaDocs. This work is released under the GPL 3.0 license. No warranty of any kind is offered.

eSense, ThinkGear, MDT, NeuroBoy and NeuroSky are trademarks of NeuroSky Inc.