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Digital Signal Processing of Spatio-Temporal Electroencephalogram (EEG) Patterns Associated With Voluntary Motion Preparation

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Digital Signal Processing of Spatio-Temporal Electroencephalogram (EEG) Patterns Associated With Voluntary Motion Preparation  

 

Method:  When an individual is not involved in planning a voluntary motion, the EEG measured at the side of the head (over the Supplementary Motor Area (SMA)) of the cortex seems to be dominated by an EEG signal that is of near sinusoidal rhythm at about 8-12Hz in frequency.  This rhythm, commonly termed the "mu-rhythm", can be recorded with minimal phase difference   throughout the side of the head using electrodes.  It is also known that this mu-rhythm will collapse locally and patterns of activation will appear (Readiness Potentials) which are specific to neuronal populations involved in the preparation of certain movements.

Presently the approach taken in the development of the Brain-Computer Interface (BCI) system involves the ability to recognize two characteristic EEG patterns (finger vs. toes) from each side of the head, which will be assigned to the four  directional cusor movements (UP, DOWN, LEFT, and RIGHT).  The proposed BCI consists of two modules:

[1]  On-Line Pre-Selection (OLPS) Module:  This module will perform a continuous, real-time analysis of data, in charge of pre-selecting time intervals of the incoming EEG that are likely to correspond to one of the target forms of motion preparation.  It should be able to detect a mu-rhythm collapse and send as output the EEG data corresponding to the neuronal pattern.

[2]  Command Verifiaction & Identification Module:  The pre-selected windows of data corresponding to that portion of the collapsed mu-rhythm will be subjected to more specific analysis in this module, to determine if one of the four cursor commands is present or if none can be verified in the segment.  Dynamic neural networks will be used, such as the Time Delay and Gamma neural networks, to classify the EEG patterns.

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Hypothesis

Modules

Preliminary Results: Mu detection

Preliminary Results: Readiness Potentials Classification