This article presents an energy-efficient and flexible multichannel Electroencephalogram (EEG) artifact identification network and its hardware using depthwise and separable convolutional neural networks. EEG signals are recordings of the brain activities. EEG recordings that are not originated from cerebral activities are termed
artifacts
. Our proposed model does not need expert knowledge for feature extraction or pre-processing of EEG data and has a very efficient architecture implementable on mobile devices. The proposed network can be reconfigured for any number of EEG channel and artifact classes. Experiments were done with the proposed model with the goal of maximizing the identification accuracy while minimizing the weight parameters and required number of operations. Our proposed network achieves 93.14% classification accuracy using an EEG dataset collected by 64-channel BioSemi ActiveTwo headsets, averaged across 17 patients and 10 artifact classes. Our hardware architecture is fully parameterized with number of input channels, filters, depth, and data bit-width. The number of processing engines (PE) in the proposed hardware can vary between 1 to 16, providing different latency, throughput, power, and energy efficiency measurements. We implement our custom hardware architecture on Xilinx FPGA (Artix-7), which on average consumes 1.4 to 4.7 mJ dynamic energy with different PE configurations. Energy consumption is further reduced by 16.7× implementing on application-specified integrated circuit at the post layout level in 65-nm CMOS technology. Our FPGA implementation is 1.7 × to 5.15 × higher in energy efficiency than some previous works. Moreover, our Application-Specified Integrated Circuit implementation is also 8.47 × to 25.79 × higher in energy efficiency compared to previous works. We also demonstrated that the proposed network is reconfigurable to detect artifacts from another EEG dataset collected in our lab by a 14-channel Emotiv EPOC+ headset and achieved 93.5% accuracy for eye blink artifact detection.
Domain Wall Memory based Convolutional Neural Networks for Bit-width Extendability and Energy-Efficiency