scholarly journals A Dry Electrode Cap and Its Application in a Steady-State Visual Evoked Potential-Based Brain–Computer Interface

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1080 ◽  
Author(s):  
Xiaoting Wu ◽  
Li Zheng ◽  
Lu Jiang ◽  
Xiaoshan Huang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
State Monitoring ◽  
Contact Impedance ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
Electroencephalogram Eeg ◽  
The Brain

The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a novel EEG cap with concise structure, easy adjustment size, as well as independently adjustable electrodes. The cap can be rapidly worn and adjusted in both horizontal and vertical dimensions. The dry electrodes on it can be adjusted independently to fit the scalp as quickly as possible. The accuracy of the BCI test employing this device is higher than when employing a headband. The proposed EEG cap makes adjustment easier and the contact impedance of the dry electrodes more uniform.

scholarly journals Brain–Computer Interface Spellers: A Review

2018 ◽  
Vol 8 (4) ◽  
pp. 57 ◽  
Author(s):  
Aya Rezeika ◽  
Mihaly Benda ◽  
Piotr Stawicki ◽  
Felix Gembler ◽  
Abdul Saboor ◽  
...  
Keyword(s):  
User Interfaces ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signal ◽  
Brain Signals ◽  
Signal Features ◽  
Communication Method ◽  
Electroencephalogram Eeg

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

scholarly journals Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 772 ◽  
Author(s):  
Gaetano Gargiulo ◽  
Paolo Bifulco ◽  
Mario Cesarelli ◽  
Alistair McEwan ◽  
Armin Nikpour ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Alpha Wave ◽  
Bill Of Materials ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
And Performance ◽  
Volunteer Participant ◽  
Analogue To Digital ◽  
The Brain

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

A novel monitor for practical brain-computer interface applications based on visual evoked potential

2021 ◽  
pp. 1-13
Author(s):  
Hamidreza Maymandi ◽  
Jorge Luis Perez Benitez ◽  
F. Gallegos-Funes ◽  
J. A. Perez Benitez
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visual Evoked

SSVEP-based Brain-computer Interface for Part-picking Robotic Co-worker

2021 ◽  
pp. 1-13
Author(s):  
Yao Li ◽  
T. Kesavadas
Keyword(s):  
Human Subject ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Industrial Robots ◽  
Computer Interface ◽  
Human Cognition ◽  
Grasp Planning ◽  
Planning Algorithm

Abstract One of the expectations for the next generation of industrial robots is to work collaboratively with humans as robotic co-workers. Robotic co-workers must be able to communicate with human collaborators intelligently and seamlessly. However, industrial robots in prevalence are not good at understanding human intentions and decisions. We demonstrate a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) which can directly deliver human cognition to robots through a headset. The BCI is applied to a part-picking robot and sends decisions to the robot while operators visually inspecting the quality of parts. The BCI is verified through a human subject study. In the study, a camera by the side of the conveyor takes photos of each part and presents it to the operator automatically. When the operator looks at the photo, the electroencephalography (EEG) is collected through BCI. The inspection decision is extracted through SSVEPs in EEG. When a defective part is identified by the operator, the signal is communicated to the robot which locates the defective part through a second camera and removes it from the conveyor. The robot can grasp various part with our grasp planning algorithm (2FRG). We have developed a CNN-CCA model for SSVEP extraction. The model is trained on a dataset collected in our offline experiment. Our approach outperforms the existing CCA, CCA-SVM, and PSD-SVM models. The CNN-CCA is further validated in an online experiment that achieves 93% accuracy in identifying and removing a defective part.

scholarly journals Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1124-1138
Author(s):  
Yue Zhang ◽  
Shane Q. Xie ◽  
He Wang ◽  
Zhiqiang Zhang
Keyword(s):  
Steady State ◽  
Data Analytics ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visual Evoked
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