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.

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 Controlled Voice Assistant

2020 ◽  
Vol 8 (6) ◽  
pp. 2370-2377
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Project Work ◽  
Daily Lives ◽  
Computer Interfaces ◽  
The Disabled ◽  
And Performance ◽  
The Brain ◽  
Bci System

A brain-controlled robot using brain computer interfaces (BCIs) was explored in this project. BCIs are systems that are able to circumvent traditional communication channels (i.e. muscles and thoughts), to ensure the human brain and physical devices communicate directly and are in charge by converting various patterns of brain activity to instructions in real time. An automation can be managed with these commands. The project work seeks to build and monitor a program that can help the disabled people accomplish certain activities independently of others in their daily lives. Develop open-source EEG and brain-computer interface analysis software. The quality and performance of BCI of different EEG signals are compared. Variable signals obtained through MATLAB Processing from the Brainwave sensor. Automation modules operate by means of the BCI system. The Brain Computer Interface aims to build a fast and reliable link between a person's brain and a personal computer. The controls also use the Brain-Computer Interface for home appliances. The system will integrate with any smartphones voice assistant.

Dry Electrode Based Wearable Wireless Brain–Computer Interface System

2011 ◽  
Vol 2 (3) ◽  
Author(s):  
Guo Kai ◽  
Pei WeiHua ◽  
Wang Yu ◽  
Xu Bing ◽  
Gui Qiang ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Skin Preparation ◽  
Wireless Transmitter ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
Application Specific Integrated Circuit ◽  
Application Specific ◽  
Bci System

Brain–computer interface (BCI) technology is a key issue in neural engineering, which can manipulate machine by electroencephalography (EEG). An important question surrounding the use of the BCI is the design of a wearable electroencephalography recording and processing equipment. We report the design and fabrication of a novel system based on dry electrodes, in which skin preparation and application of electrolytic gel are not required. In this study, an EEG-based BCI system, which includes a wireless transmitter module and an receiver module was designed, EEG is acquired using dry electrodes, amplified and processed by an application-specific integrated circuit (ASIC), and transmitted to the receiver by RF chip. The BCI system can obtain the subject’s degree of concentration, and those trained subjects have the ability of controlling the machine by changing their EEG signals. A experiment that controlling a toy car using the BCI system is successfully performed. The wearable transmitter module weighs 39 g only and easy to wear. The transmitter consumes 60 mW of dc power and generates an output power of 0 dBm. The BCI system is suitable for long-term EEG monitoring in users’ daily life. This system is feasible for further extension.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

scholarly journals Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

Neurology ◽  
2018 ◽  
Vol 91 (3) ◽  
pp. e258-e267 ◽  
Author(s):  
Jonathan R. Wolpaw ◽  
Richard S. Bedlack ◽  
Domenic J. Reda ◽  
Robert J. Ringer ◽  
Patricia G. Banks ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Amyotrophic Lateral Sclerosis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
It Use ◽  
Technical Problems ◽  
Rapid Disease Progression ◽  
And Performance ◽  
Lateral Sclerosis

ObjectiveTo assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.MethodsOf 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28–79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.ResultsOver subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.ConclusionThe Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

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