Predictive BCI Speller based on Motor Imagery
- 1 Part 1: Project profile
- 2 Part 2: Project description
- 3 Part 3: Project tracking
- 4 Part 4: References
- 5 Part 5: Links
Part 1: Project profile
BCI based on Motor Imagery
Project short description
This project is aimed is to control an external device through the analysis of brain waves measured on the human scalp.
Start date: 01/05/2008
Other Politecnico di Milano people
- Rossella Blatt (phd student)
Students currently working on the project
- Tiziano D'Albis (master student)
Laboratory work and risk analysis
Laboratory work for this project will be mainly performed at AIRLab-IIT/Lambrate. The main activity consists in the acquisition of brain signals through an EEG amplifier for on-line or off-line processing. This is a potentially risky activity since there is an electrical instrumentation that is in direct contact with the human body. It is thus important to keep the system isolated from the power line. The EEG amplifier (as all biomedical instrumentations) is certified by the vendor to be isolated and the acquired data are transferred to the PC using an optic fiber connection . Anyhow for increased safety the PC and any other electronic device connected to the system must be disconnected from the power line. Standard safety measures described in Safety norms will be followed.
Part 2: Project description
The goal of this project is to implement a BCI system supporting people with motor disabilities to communicate effectively. The main idea is to adopt the "virtual keyboard" paradigm in which a series of symbols are selected by means of a BCI interface.
This kind of communication can be included in the class of Alternative and Augmentative Communication (AAC) and the main issue with it is in the need of minimizing the number of selections required by the user while running the application. Indeed the selection process could be costly (both in terms of time and effort) for the target users of these kinds of systems.
The adoption of a BCI interface, moreover, sets also a limit to the number of alternative choices available for each selection. This limit depends on the specific BCI paradigm used and on the way the selection process is carried out. Using a scanning approach, for example, it is possible to overcome this kind of limit, but introducing the drawback of much longer selection delays.
On the other side with a direct selection it is possible to classify effectively a very limited number of states, with common BCI paradigms this limit is usually set to 2,3 or 4. The Motor Imagery paradigm, instead, is somehow different because it could be used to translate sensymotor rhythms in the continuous movement of a cursor in 2 dimensions. In this case it is possible to associate a choice with a target on the screen and perform a selection just hitting a screen target with the cursor. With this kind of approach the number of different choices is limited only by the precision of the cursor movement that can be achieved.
The main idea of this thesis is group the alphabetical letters in sets and than recursively expand them (with a selection based on motor imagery) till a single letter is selected. Moreover we want to aid the selection process displaying the most probable letters in sets with a low number of elements, thus minimizing the selection steps required by the user. Finally we want to implement also a word completion and prediction algorithm that would give also the possibility to select a whole word in one shot.
Part 3: Project tracking
- 01/04/2008: started studying literature about BCI and Motor Imagery
- 01/05/2008: started acquiring data with BCI2000 - performing initial sessions
- 01/06/2008: stated performing feedback sessions with cursor movement in 1D
- 01/09/2008: first idea about the predictive BCI speller
- 10/09/2008: started studying literature about AAC and NLP
- 24/09/2008: first written document about the main thesis topic: File:Predictive BCI speller v1.pdf (ITA)
Part 4: References
- Control of two-dimensional movement signals by a noninvasive brain-computer interface in humans, Wolpaw J.R., McFarland J., PNAS, vol. 101, no. 51, december 2004, pages 17849-17854.
- Brain Computer interfaces for communication and control, Wolpaw J.R., Birbaumer N., McFarland D., Pfurtsheller G., Vaughan T., Clinical Neurophysiology 113, 2002, 767-791
- EEG based communication: prospects and problems, Vaughan T., Wolpaw J.R., Donchin E., IEEE transactions on rehabilitation engineering, vol. 4, no. 4, december 1996, pages 425-430.