 |
|
The Brain-Computer Interface (BCI) A Brain-Computer Interface (BCI) provides a new communication
channel between the human brain and the computer. Mental activity leads
to changes of electrophysiological signals like the Electroencephalogram
(EEG) or Electrocorticogram (ECoG). The BCI system detects such changes
and transforms it into a control signal which can, for example, be used
as spelling device or to control a cursor on the computer monitor. One
of the main goals is to enable completely paralyzed patients (locked-in
syndrome) to communicate with their environment.
Two learning systems An interesting question for the development of a BCI is how to handle two learning systems: The machine should learn to discriminate between different patterns of brain activity as accurate as possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals. BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification as well as the generation of neurofeedback for a successful training of the user has to run in real-time. g.BCIsys - The complete development and research system g.tec provides complete MATLAB-based development/research systems including all hard- and software components needed for data acquisition, real-time and off-line data analysis, data set classification and for providing neurofeedback. The BCI system can be realized with g.MOBIlab+, g.USBamp or g.BSamp. g.MOBIlab+ is available with up to 8 EEG channels and is portable and available with wireless signal transmission. g.USBamp is available for 16-256 EEG channels and transmits the data over USB to the PC or notebook. g.BSamp is availabe for 8, 16 to 80 channels. The software package High-Speed Online Processing under SIMULINK allows to read the biosignal data directly into SIMULINK. SIMULINK blocks are used to visualize and store the data. The parameter extraction and classification is performed either with standard SIMULINK blocks, with the g.RTanalyze library or with self-written S-functions. After the EEG data acquisition the data can be analyzed with g.BSanalyze, the EEG and classification toolbox. Ready-to-use BCI sample applications allow to make state-of-the-art BCI experiments within a few yours. Highlights
|
The g.tec BCI research platform wins the European ICT Prize 2007. Scientific approaches During the past decade many groups all over the world intensified their work in the field of BCI-research. The different methods published in international scientific journals display the wide range of possible solutions for the problem. The following link provides an overview on some of the most prominent methods and authors working on this hot topic: State of the Art in BCI research To support your start into the fascinating world of Brain-Computer Interface research see some literature here: Publications Some of the most commonly used strategies to realize a BCI are: |
|
 |
Imagery of movements of different limbs cause changes in oscillatory EEG activity over sensorimotor areas of the central cortex. These changes can be classified by weighting spectral parameters of different frequency bands for different electrode positions. |
|
A P300 component is produced if an unlike event occurs. The P300 occurs about 300 ms after the event and has to be detected by specific algorithms. The P300 components are mainly used to create a spelling device for paralyzed patients. |
|
Slow shifts of cortical potentials occur when a subject performs an imagery of expecting an event (like waiting for a traffic light turning to green). The resulting DC-shift can be used for biofeedback to improve the training effects and to generate a control signal for communication. |
|
Also other mental tasks such as mental arithmetic, mental cube rotation or attention versus relaxation are used to produce characteristic changes of EEG patterns. One attempt has also been not to guide the subjects with any strategy but use specific EEG-biofeedback, so that the user attempts to find his/her own strategy for producing the required changes in the EEG. |
|
Another method uses steady-state visually evoked potentials (SSVEP) from flickering light sources. Directing attention to a source with a specific flicker frequency enlarges evoked components in the EEG with the same frequency. |
It can be stated that none of all the methods used in BCI
research yields perfect results but the performance was significantly
improved by new parameter-extraction algorithms and pattern-recognition/classification
methods. The usability of a BCI has to be evaluated with respect to the
following aspects: |
|
|
accuracy (classification error, hits vs. false, false positives, ...) |
|
information transfer (decision speed, bit/min, ...) |
|
number of classes (idling vs. activation of 1 class, 2 or more different classes, ...) |
|
operation mode (synchronous: predefined decision intervals, asynchronous: free decision time) |
|
intended application (spelling device, control of orthotic/prosthetic device, environmental control) |
| g.BCIsys comes with ready-to-use
example BCI-paradigms based on changes in oscillatory EEG activity induced
by two different types of motor imageries and paradigms based on the P300
component.
|
||
Example Paradigm: motor imagery |
||
| Step 1 (initial training): Based on a cue (arrow on the screen pointing to the left or to the right) the subject performs left and right hand movement imageries (duration 3-4 seconds). To train the classifier between 40 and 160 trials are recommended. EEG should be recorded from electrode positions C3 and C4. |
|
Step 3 (training with neuro-feedback): |
| Step 2 (offline analysis
and classifier generation): The recorded data are processed with g.BSanalyze. Alpha and beta bandpower parameters for both EEG channels are computed to build the feature matrix. Linear discriminant analysis is used for classification and cross-validation shows the usability of the best classifier. |
Step 4 (classifier update): The continuous feedback should help the subject to train the motor imageries leading to a correct classification. To improve the performance the classifier should be updated after some successful sessions. A new classifier can also be computed from the data of a feedback session. Offline analysis of the recorded data supports feature optimization. |
|
|
||
Example Application: Ping Pong Everybody knows the famous Ping-Pong game that was played in the seventies on TV sets. In this example two persons are connected to the BCI system and each one is controlling the racket with motor imagery. The racket moves upwards by left hand movement imagination and downwards by right hand movement imagination. |
||
The algorithm extracts EEG bandpower in the alpha and beta ranges of two EEG channels per person. Therefore in total 4 EEG channels are analyzed and classified. |
||
Example Paradigm: P300 spelling device The P300 paradigm presents e.g. 36 letters in a 6 x 6 matrix on the computer monitor. Each letter is flashing up in a random order and the subject has to be concentrated on the letter it wants to write. As soon as the corresponding letter is flashing up a P300 component is produced inside the brain. The algorithms are analyzing the EEG data and select the letter with the highest P300 component. Then this letter is written onto the screen. Normally between 2-20 flashes per letter are required to achieve a high accuracy. The number is dependent on the electrode position used, the training level of the subject and the individual height of the P300 response of the subject. In Copy Spelling mode first a word or a sentence has to be entered. The task of the subject is to copy exactly each letter as shown in the following picture:
This allows to calculate the error rate of the spelling device and is mainly used for the training of the subject. In Free Spelling mode the subject is just concentrated on the letter it wants to write. The BCI system searches for the letter with the highest P300 response and writes the letters onto the screen. This mode can be used by patients for communication, to answer questions or to express needs.
For this system no a priori subject specific EEG data acquisition is necessary. The new subject is just connected with EEG electrodes to the BCI system and the algorithms search for the corresponding features. Some subjects can already spell words within a few minutes. Further information and literature for P300 g.tec brings the first patient-ready BCI on the market. The EEG-based spelling system is called intendiX and enables the user to select keys from a matrix just by thinking. |
||
Example: ECoG recording g.USBamp is a CF recording device and therefore it can also be used for invasive recordings. The picture below shows an ECoG electrode grid overlaying the brain. The electrode grid is connected to the BCI system for real-time analysis and paradigm presentation.
ECoG electrode grid photo by Gerwin Schalk (Wadsworth Center, Albany, USA) and Kai Miller, Jeff Ojemann (University of Washington). g.USBamp has the necessary CE and FDA certification for invasive recordings. |
| Example Paradigm: Robot Control with SSVEP (Steady State Visual Evoked Potentials) In this example the user gets stimulated by four lightsources flickering with different frequencies. These lightsources can be LEDs or flashing fields presented on a computer monitor.
|
In this example the P300 based BCI system was connected to a Virtual Reality (VR) system. The virtual 3D representation of the smart home had different control elements (TV, music, windows, heating system, phone,…) and allowed the subjects to move through the apartment. Some tasks could be done, like playing music, watching TV, open doors, moving around... Therefore seven control masks were created: a light mask, a music mask, a phone mask, a temperature mask, a TV mask, a move mask and a go to mask. The controlling mask for the TV is shown in Fig 1. Each mask contained 13 to 50 commands. In Fig. 2 the Goto mask is shown. The mask gives a bird’s eye view of the apartment with characters at specific positions. For going e.g. to the couch you have to focus on the "B".
A P300 based BCI system is optimally suited to control smart home applications with high accuracy and high reliability. As the BCI and P300 method works fine with the Powerwall (shown in Fig.3), the system can act as cheap test environment for real smart homes for paralyzed patients. |
Example Paradigm: The Multimodal Brain Orchestra
|
BCI in virtual environments |
Tutorials Brain Computer Interface Manual (PDF 1.6 MByte) - The document describes the steps to classify the brain-computer interface EEG data with the bandpower algorithm and a linear discriminant analysis. P300 Spelling Device with g.USBamp and Simulink (PDF 993 kByte) - Description of the steps to perform P300 spelling device experiments with g.USBamp. Furthermore the off-line P300 analysis is descripted. P300 Seplling Device with g.MOBIlab+ and Simulink (PDF 1.3 MByte) - same as above but with g.MOBIlab+ technology Brain Computer Interface with g.USBamp (PDF 1.05 MByte) - The document describes real-time BCI experiments with motor imagery Brain Computer Interface with g.MOBIlab+ (PDF
1.07 MByte) - same as above but with g.MOBIlab+ technology Locomotion by thoughts (MPG 12,1 MByte). The movie shows an experiment carried out together with University of Technology Graz (Robert Leeb, Gert Pfurtscheller) and University College London (Mel Slater, Doron Friedman) within the EU project PRESENCCIA. Music generation by brain waves (MPG 2,7 MByte). Project of University of Plymouth (Eduardo Miranda). The Multimodal Brain Orchestra (MOV 13 MByte) realized by specs.upf.edu Robot Control with Steady State Visual Evoked Potentials (MPG 12,5 MByte) by g.tec, Austria Brain-Computer Interface on CNN (MPG 10,2 MByte) Brain Power on 60 Minutes Controlling a smart home with the brain-computer interface.
P300-based BCI for Internet Browsing Links: Motor Imagery based Essex online BCI: http://dces.essex.ac.uk/Research/BCIs/AABAC.html |
Prerequisite MATLAB, Simulink and Signal Processing Toolbox (Release 2008b) |
Up