A Novel Steady-State Visually Evoked Potential-Based Brain-Computer-Interface Paradigm to Steer a Humanoid Robot

2014 ◽  
Author(s):  
Nannan Zhang ◽  
Jun Jiang ◽  
Jingsheng Tang ◽  
Zongtan Zhou ◽  
Dewen Hu
Keyword(s):  
Steady State ◽  
Humanoid Robot ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visually Evoked Potential

scholarly journals Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 681
Author(s):  
Bor-Shyh Lin ◽  
Bor-Shing Lin ◽  
Tzu-Hsiang Yen ◽  
Chien-Chin Hsu ◽  
Yao-Chin Wang
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Signal Quality ◽  
Eeg Signal ◽  
Visually Evoked Potential ◽  
Information Transfer Rate ◽  
Field Programmable

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

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

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5474 ◽  
Author(s):  
Dalin Yang ◽  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Home Automation ◽  
Eye Blink ◽  
Eye Blinks ◽  
Visually Evoked Potential ◽  
Automation Control ◽  
Bci System

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.

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