EEG and Motor Imagery

Today I read “Motor Imagery Classification by Means of Source Analysis for Brain Computer Interface Applications” paper from Lei Qin, Lei Ding, and Bin He.

They made a pilot study to classify motor imagery for Brain-Computer Interface applications by analysing scalp-recorded EEGs.

Their subjects were tested on hand movement imagination. Their source analysis approach in combination with signal preprocessing techniques for classification of motor imagery returned positive results in the order of 80%. They concluded that a better classifier with some training procedure could be introduced to improve the approach.

So far, EEG seems to be one of the best approaches for Brain-Computer Interfaces; but only time (or papers) will tell. They seem to be the best non-invasive BCI interface, and seem to return results good enough for classification of motor imagery and other evoked responses, as I’ve understood from the readings in my other article.

If this week allows, I feel the next paper can have a significant impact on my knowledge about the past, present and future of Brain-machine interfaces.

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Author: Pedro Oliveira

I am a Master of Informatics and Computer Engineering, graduated from the Faculty of Engineering of the University of Porto. I am also an amateur photographer, volunteer at BEST and former member of TEUP. I am curious and creative, organized, assertive but reserved, friendly and caring. I like writing, music, photography, Japanese culture, fitness, sports and media entertainment. I am interested in Brain-Computer Interfaces, Virtual Reality, Game Design and Development, PR, Marketing, Multimedia, Mobile and Web Development.

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