A generalized preprocessing and feature extraction platform for scalp EEG signals on FPGA

Abstract

Brain-computer interfaces (BCIs) require real-time feature extraction for translating input EEG signals recorded from a subject into an output command or decision. Owing to the inherent difficulties in EEG signal processing and neural decoding, many of the feature extraction algorithms are complex and computationally demanding. Presently, software does exist to perform real-time feature extraction and classification of EEG signals. However, the requirement of a personal computer is a major obstacle in bringing these technologies to the home and mobile user affording ease of use. We present the FPGA design and novel architecture of a generalized platform that provides a set of predefined features and preprocessing steps that can be configured by a user for BCI applications. The preprocessing steps include power line noise cancellation and baseline removal while the feature set includes a combination of linear and nonlinear, univariate and bivariate measures commonly utilized in BCIs. We provide a comparison of our results with software and also validate the platform by implementing a seizure detection algorithm on a standard dataset and obtained a classification accuracy of over 96%. A gradual transition of BCI systems to hardware would prove beneficial in terms of compactness, power consumption and much faster response to stimuli.