A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Abstract

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

“…Eight articles used Emotiv equipment [ 5 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], five articles used g.Tec equipment [ 51 , 52 , 53 , 54 , 55 ], three articles used Brain Products equipment [ 56 , 57 , 58 ], two articles used Compumedics Neuroscan equipment [ 59 , 60 ], and two articles used NeuroSky equipment [ 61 , 62 ]. For the equipment for Cognionics Inc. [ 63 ], Electrical Geodesics Inc. [ 64 ], Neuroelectrics [ 65 ], and Advanced Brain Monitoring [ 66 ], one article each used their equipment.…”

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods

“…In general terms, the method based on CNNeeg1-1 for imagined vowels classification does not require demanding training processes, as in the case of imagined motor tasks [ 56 ]; it does not require a rigorous attention process like in SSVEP [ 57 , 58 ], P300, or imagined motor tasks [ 59 ]; it does not require an external stimulus like SSVEP or P300 [ 60 , 61 ]; and it does not require cognitive tasks that generate muscular or cognitive fatigue as in imagined motor tasks [ 56 , 59 ]. Consequently, the CNNeeg1-1 method developed in this study has the potential to use other language components and to be applied in such relevant fields as BCI device control.…”

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

“…As a noninvasive neuroimaging modality, EEG is one of the oldest techniques used to measure neural activation in the human brain for diagnosis or brain–computer interface (BCI) purposes ( Naseer and Hong, 2013 ; Khan and Hong, 2017 ; Tanveer et al, 2019 ). Since they are portable and have the advantage of higher temporal resolution, EEG-based BCI applications have been widely designed for daily use (e.g., home automation control devices, EEG-based wheelchairs, brain disorder detection platforms) ( Kim et al, 2019 ; Lee T. et al, 2020 ; Rashid et al, 2020 ; Yang et al, 2020b ). EEG measurements are based on electrical potential differences between different electrodes on the scalp.…”