FPGA-Based Hardware Accelerator on Portable Equipment for EEG Signal Patterns Recognition

Abstract

Electroencephalogram (EEG) is a recording of comprehensive reflection of physiological brain activities. Because of many reasons, however, including noises of heartbeat artifacts and muscular movements, there are complex challenges for efficient EEG signal classification. The Convolutional Neural Networks (CNN) is considered a promising tool for extracting data features. A deep neural network can detect the deeper-level features with a multilayer through nonlinear mapping. However, there are few viable deep learning algorithms applied to BCI systems. This study proposes a more effective acquisition and processing HW-SW method for EEG biosignal. First, we use a consumer-grade EEG acquisition device to record EEG signals. Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) methods will be used for data preprocessing. Compared with other algorithms, the CWT-CNN algorithm shows a better classification accuracy. The research result shows that the best classification accuracy of the CWT-CNN algorithm is 91.65%. On the other side, CNN inference requires many convolution operations. We further propose a lightweight CNN inference hardware accelerator framework to speed up inference calculation, and we verify and evaluate its performance. The proposed framework performs network tasks quickly and precisely while using less logical resources on the PYNQ-Z2 FPGA development board.