Title: A brain-computer interface system for smart home control based on single trial motor imagery EEG

Authors: Wei Zhuang; Yixian Shen; Lu Li; Chunming Gao; Dong Dai

Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, No. 219 Ningliu Road, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, No. 219 Ningliu Road, Nanjing, 210044, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Engineering Research Center of Digital Forensics, Ministry of Education, No. 219 Ningliu Road, Nanjing, 210044, China ' School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA 98402, USA ' School of Cyber Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing, 210096, China

Abstract: In recent years, researches on brain signal recognition and brain-computer interface control have made great progress. By analysing electroencephalogram (EEG), a specific brain activity can be detected and the signal can be used to control smart devices and help people to complete difficult and complicated tasks, especially for people with disabilities. This paper presents the design and implementation of a novel brain-computer interface system for smart home control using single trial motor imagery EEG. The system adopts STM32 Microcontroller Unit (MCU) and ThinkGear Asic Module (TGAM) to realise the acquisition and recognition of EEG signals. It can transfer the signals to portable devices through Bluetooth modules. Three main EEG features including Alpha, Beta, and Gamma waves are discussed. It is tested during simple actions such as blinking in various situations. The experimental results show that the implemented system is suitable for extracting specific EEG signals to control smart home devices.

Keywords: EEG recognition; EEG features; EEG detection; blinking detection; wearable EEG device; STM32 embedded control; TGAM; ThinkGear Asic module; smart home control.

DOI: 10.1504/IJSNET.2020.111780

International Journal of Sensor Networks, 2020 Vol.34 No.4, pp.214 - 225

Received: 31 Mar 2020
Accepted: 25 Apr 2020

Published online: 14 Dec 2020 *

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