ADAPTIVE MULTIRESOLUTION ANALYSIS BASED EVOKED-POTENTIAL FILTERING

Evoked potentials (EPs) are electrical activities of the brain synchro nised with external stimuli. They have proved valuable for the underst anding of the functioning of the brain and in investigating several br ain related disorders, EPs are usually obscured by the background elec troencephalogram (EEG) and thus require appropriate filtering. As the frequency spectra of the EEG and EPs overlap, the application of deter ministic filters on their own is usually inadequate. Synchronised aver aging improves the signal-to-noise ratio; however it inhibits measurem ent of the important variations which develop from one EP recording or trial to the next. The presence of these variations also makes an ave raged EP a distorted version of an EP which evolves with time. A novel adaptive filtering method algorithm based on the wavelet transform me thod of multiresolution analysis (MRA) was developed and was successfu lly used for single trial recovery of a type of EP known as the contin gent negative variation (CNV). Both stimulated and real CNV waveforms were real CNV waveforms simulated and were processed. A technique to e valuate the effectiveness of the developed method was devised and was used to select the best orthogonal filter among Daubechies, Coifman an d Symmlet for the adaptive MRA based filtering operation. The techniqu e enabled the magnitude of the background EEG to be reduced by a facto r of 5 while preserving the main features of the CNV waveform.