Purpose-based filtering approach for neural interfaces

Background and aims: Neural interfaces are one of the main tools for investigating potential treatments and designing neural prosthetics for many types of neurological diseases. Neural microarrays and probes consist of many electrodes for stimulating and recording purposes simultaneously. The multi-functional neural interface can suffer from many types of artefacts and noise like long-term use, environment, surrounding instruments and living subjects. This paper aims to design an adaptive filter to keep the most important information of brain signals in all channels.

Methods: The main approach consists of enhancing the band-pass selection in the Kaiser Window FIR filter based on the most influencing spectrum bands of brain signals. This approach can achieve a self-update of parameters using a fixed interval. The study contains the ECoG and LFP signals of nine epileptic rats using the 10-min of baseline.

Results: The accurate selection of frequencies in any filter, taking into account the common information among all channels, increase the reliability of analyzed signals and enhances the extracted features for evaluation stages. On other hand, the probabilistic distribution of the most influencing power bands in all channels helps in keeping the most desirable information in terms of neurological events.