A novel approach for electroencephalogram (EEG) processing is presented. Along with the theoretical development of stochastic signal processing techniques, three application areas are suggested: general anaesthesia monitoring, altered states of consciousness e.g. hypnosis/sedation and sports performance medicine e.g. training to be in “the zone”. A number of methods already exist to analyze EEG signals. However, they are generally considered to be difficult to interpret and suffer from lack of noise immunity. Spectral analysis certainly aids interpretation but most methods employing the Fast Fourier Transform (FFT) suffer from some well known drawbacks, such as spectral leakage and cross modulation. The method developed here is a modified form of the auto-correlation Yule-Walker algorithm we have named the Bristol Process. It has been found to be stable and able to recover rapidly from even large scale electromagnetic interference (such as surgical diathermy) and from electrical artefacts induced by eye-blinks and body movement. This novel approach permits stable calculation of power relative to total power (an index) at any chosen frequency or frequency band and makes interpretation of EEG easier. Continuous recording of an index (trend) for immediate or post-event review improves interpretation and may be used in association with video recording of sports performance. Future developments are planned with a commercial organisation.
|Translated title of the contribution||Recent advances in EEG monitoring for general anaesthesia, altered states of consciousness and sports performance science|
|Title of host publication||3rd IEE International Seminar on Medical Applications of Signal Processing, 2005|
|Publisher||Institution of Engineering and Technology (IET)|
|Publication status||Published - 2006|
Griffiths, MJ., Grainger, P., Cox, MV., & Preece, AW. (2006). Recent advances in EEG monitoring for general anaesthesia, altered states of consciousness and sports performance science. In 3rd IEE International Seminar on Medical Applications of Signal Processing, 2005 Institution of Engineering and Technology (IET). https://doi.org/10.1049/ic:20050322