Hardware Implementation of EEG Classifier Using LDA

In Electroencephalography (EEG) classifiers, processing of EEG signals consists of extracting features from EEG signal and then processing those features to classify the signals. As of today, in most EEG processing systems, the classification part is done on software platform even though the feature extraction is done on hardware. Hence this require an operating system which limits the development of portable medical devices. This paper presents the design and implementation of feature classification in EEG signal processing system on Field Programmable Gate Array (FPGA) hardware platform. The classification is done with two-class Linear Discriminant Analysis (LDA), based on the features extracted using Discrete Wavelet Transform (DWT), for EEG signals obtained through PhysioNet website. The final design has minimum resource utilization, hence is able to implement on Basys 3 Artix-7 FPGA Board platform using SystemVerilog with SO % hardware accuracy. From this result, it is concluded that this design is suitable for developing low cost, marketable products like sleep detectors for automobile divers. Nevertheless, ultimate the goal is to design a full system, which extracts features and classify EEG signals, that can be implemented without using software platform.