Embedded realisation of amplitude-phase adaptive filter for bio-potential signals

Superposition of noise with the bio-potential signals causes plethoric information loss and misinterpretation in human-computer interference systems. Here, we have proposed a novel method to design a digital signal filter which is capable of filtering four major bio-potential signals viz. Electroencephalography (EEG), Electrooculography (EOG), Electrocardiography (ECG) and Electromyography (EMG). Different sampling frequencies for different bio-potential signals are manually selected through two select lines added as external inputs to the microcontroller based system. The filter has two sections in cascade, a conventional sixth order digital filter followed by an adaptive interference canceller (AIC). The AIC has a remarkable property of eliminating most of the interfering power line noise adaptively without consuming much time or space complexity of the embedded processor used. A variant of Least Mean Square (LMS) filtering algorithm called Amplitude-Phase Adaptive LMS (APALMS) is implemented here. Convergence behaviors of the adaptive parameters are simulated and finally verified on real time biopotential signals extracted from different subjects using low cost embedded processor. In this paper, we have found that our proposed filter transition frequencies are small with least variation in adaptation time.