An efficient EEGNet processor design for portable EEG-Based BCIs. (February 2022)

• Record Type: Journal Article
• Authors: Feng, Lichen; Yang, Liying; Liu, Shubin; Han, Chenxi; Zhang, Yueqi; Zhu, Zhangming
• Is Part Of: Microelectronics journal. Volume 120(2022)
• Publication Date: 2022-02
• Subjects: Electroencephalography -- Brain-computer interface -- Convolutional neural network -- Computation efficiency; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2021.105356
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: Brain-computer interfaces (BCIs) could manipulate devices through non-invasive electroencephalogram (EEG) signal. A compact deep convolutional neural network model, EEGNet, and its hardware implementation, has been developed for obtaining generalization towards different BCI paradigms. However, the exist design consumes huge amount of area and power resources. BCI devices are used dynamically on portable devices in most scenario, which has restricted limitations on resource consumption. Hence, in this paper, we present an efficient EEGNet processor design to accomplish accurate and real-time EEG classification. The proposed design reuses the processing elements in a convolutional channel, and simplifies the exponential functions in activation layer and classifier layer to improve efficiency. The experimental results of FPGA implementation show that the proposed design consumes 2.54% in terms of area, and 3.66% in terms of power consumption comparing to the previous work, which is more suitable for portable BCI devices.