May I know what are the EEG channels that are considered as most important in BCI studies. I mean for example if the data is collected using the international standard 10-20 system, then for motor imagery based BCI, which channels we should look for classification, or for ERP detection and classification? The answer would help us to decide for artifact detection and removal in a particular EEG channel.
Any suggestion or recommended link/paper would be highly appreciated. Thanks a lot!
I would propose to look at some of the original neuroscience literature on imagined movement, e.g. by Pfurtscheller:
https://www.google.nl/search?q=pfurtscheller+motor&ie=utf-8&oe=utf-8&gws_rd=cr&ei=LoP1VLLGH8XyOOSIgIgG
and then at some papers about BCI and motor imagery, e.g. by the Berlin group:
http://doc.ml.tu-berlin.de/publications/klaus-robert.mueller
Short answer: C3 and C4 are the most commonly used sensors. The Berlin group in particular fancies a mathematical approach called common-spatial patterns, which extracts a spatial feature map that is then used for each subject. The purpose of your research, however, defines your approach. If you are an engineer, you might want to try the heavy machine learning approach. If you are a neuroscientist you might want to care more about the underlying brain region and related brain mechanism.
There are several other opinions. Please, evaluate some papers at Attachment. All the best.
I would propose to look at some of the original neuroscience literature on imagined movement, e.g. by Pfurtscheller:
https://www.google.nl/search?q=pfurtscheller+motor&ie=utf-8&oe=utf-8&gws_rd=cr&ei=LoP1VLLGH8XyOOSIgIgG
and then at some papers about BCI and motor imagery, e.g. by the Berlin group:
http://doc.ml.tu-berlin.de/publications/klaus-robert.mueller
Short answer: C3 and C4 are the most commonly used sensors. The Berlin group in particular fancies a mathematical approach called common-spatial patterns, which extracts a spatial feature map that is then used for each subject. The purpose of your research, however, defines your approach. If you are an engineer, you might want to try the heavy machine learning approach. If you are a neuroscientist you might want to care more about the underlying brain region and related brain mechanism.
Generally, you will want to use channels that contain useful information about that particular paradigm. For the P300 BCI experiments, according to my experience, the most relevant channels are Fz, Cz, and (especially) Pz. But other channels may help you to better deal with artifact removal, e.g. EOG for eye motion/blinking removal, or O1 and O2 channels for alpha waves.
That depends largely on the application for placement of electrodes, for example the frontal part of the brain is very used for imagery motor, affective and emotional signals, i.e. passive or active BCIs; unlike of the occipital region mostly used for reactive BCIs .
Thanks all, the comments are very informative and I have learned and applied these suggestions! :)
- Badges
- Science topic
- Similar topics
- Data Mining
- Classification