About Brain State Decoding in Brain-Computer Interfaces

Diagram of a typical BCI system

In general, brain-computer interface (BCI) systems make use of state-of-the-art machine learning methods, to decode ongoing brain signals in real-time. Via BCIs, users shall be enabled to type text, control a computer or wheelchair - even if they are severely motor impaired. To perform such a task, a BCI is realized using several components (as shown in Figure 1):

• Brain activity measurement: EEG, ECoG, MRI, PET, NIRS, etc
• Signal preprocessing: band-pass filtering, outlier removal, artifact correction, normalization, etc
• Feature extraction: gain relevant information from acquired data, e.g. the band power of a neural oscillatory source of interest
• Classification: determinewhich brain state the recorded signals correspond to (decode the intented action of the subject)
• Application/feedback: present the effect of a control step to the user, e.g. by typing a letter to the screen

Applications

Here some examples what can be done with a BCI system:

• Communication / text entry
• Control of a chess game
• Brain painting application
• Mind controlled pinball machine
• Mind controlled World of Warcraft
• Mind controlled robotic arm

External resources

Want to know more?

• BCI standards: Website describing BCI standards.
• BBCI: BCI lab at the TU Berlin
• Graz BCI: BCI lab at the TU Graz
• Webpage of Fabien Lotte: List of BCI-related conferences / special issues