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.

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 The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals Simultaneous Voltage and Current Measurement Instrumentation Amplifier for ECG and PPG Monitoring

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 679
Author(s):  
Jongpal Kim
Keyword(s):  
Integrated Circuit ◽  
Cardiovascular Health ◽  
Current Noise ◽  
Instrumentation Amplifier ◽  
Current Sensing ◽  
Application Specific Integrated Circuit ◽  
Time Division ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Application Specific

An instrumentation amplifier (IA) capable of sensing both voltage and current at the same time has been introduced and applied to electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for cardiovascular health monitoring applications. The proposed IA can switch between the voltage and current sensing configurations in a time–division manner faster than the ECG and PPG bandwidths. The application-specific integrated circuit (ASIC) of the proposed circuit design was implemented using 180 nm CMOS fabrication technology. Input-referred voltage noise and current noise were measured as 3.9 µVrms and 172 pArms, respectively, and power consumption was measured as 34.9 µA. In the current sensing configuration, a current noise reduction technique is applied, which was confirmed to be a 25 times improvement over the previous version. Using a single IA, ECG and PPG can be monitored in the form of separated ECG and PPG signals. In addition, for the first time, a merged ECG/PPG signal is acquired, which has features of both ECG and PPG peaks.

CONCEPTS OF ELECTRONICALLY-CONTROLLED ELECTROMECHANICAL/MECHANOELECTRICAL STEER-, AUTODRIVE- AND AUTOABSORBABLE WHEELS FOR ENVIRONMENTALLY-FRIENDLY TRI-MODE SUPERCARS

1994 ◽  
Vol 04 (04) ◽  
pp. 501-516 ◽  
Author(s):  
BOGDAN T. FIJALKOWSKI ◽  
JAN W. KROSNICKI
Keyword(s):  
Direct Current ◽  
Integrated Circuit ◽  
Environmentally Friendly ◽  
Alternating Current ◽  
Switching Frequency ◽  
Neuro Fuzzy ◽  
Interior Permanent Magnet ◽  
Application Specific Integrated Circuit ◽  
Computer Processor ◽  
Application Specific

Concepts of the electronically-controlled electromechanical/mechanoelectrical Steer-, Autodrive- and Autoabsorbable Wheels (SA2W) with their brushless Alternating Current-to-Alternating Current (AC-AC), Alternating Current-to-Direct Current-Alternating Current (AC-DC-AC) and/or Direct Current-to-Alternating Current (DC-AC)/Alternating Current-to-Direct Current (AC-DC) macroelectronic converter commutator (macro-commutator) wheel-hub motors/generators with the Application Specific Integrated Matrixer (ASIM) macroelectronic converter commutators (ASIM macrocommutators) and Application Specific Integrated Circuit (ASIC) microelectronic Neuro-Fuzzy (NF) computer (processor) controllers (ASIC NF microcontrollers) for environmentally-friendly tri-mode supercars (advanced ultralight hybrids) have been conceived by the first author and designed by both authors with the Cracow University of Technology’s Automotive Mechatronics Research and Development (R&D) Team. These electromechanical/mechanoelectrical wheel-hub motors/generators, respectively, for instance, can be composed of the outer rotor with the Interior Permanent Magnet (IPM) poles and the inner stator that has the three-phase armature winding. The macroelectronic converter commutator establishes the AC-AC cycloconverter, AC-DC rectifier-DC-AC inverter and/or DC-AC inverter/AC-DC rectifier ASIM macrocommutator. The microelectronic NF computer (processor) controller establishes the ASIC microcomputer-based NF microcontroller. By adopting continuous semiconductor bipolar electrical valves in the high-power ASIM, it has been able to increase the commutation (switching) frequency and reduce harmonic losses of the electromechanical/mechanoelectrical wheel-hub motors/generators, respectively.

scholarly journals Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingwei Zhang ◽  
Yao Hou ◽  
Rongnian Tang ◽  
Youjun Li
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Spatial Covariance ◽  
Learning Machine ◽  
Low Dimensional ◽  
Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

scholarly journals Design of ASIC Square Calculator Using AncientVedic Mathematics

2018 ◽  
Vol 7 (2.23) ◽  
pp. 464
Author(s):  
Angshuman Khan ◽  
Sudip Halder ◽  
Shubhajit Pal
Keyword(s):  
Integrated Circuit ◽  
Software Tool ◽  
Logic Gates ◽  
Digital Logic ◽  
Simple Design ◽  
Innovative Design ◽  
Circuit Asic ◽  
Vedic Mathematics ◽  
Application Specific Integrated Circuit ◽  
Application Specific

This article includes a simple design of Vedic square calculator for Application Specific Integrated Circuit (ASIC). This is a straightforward and innovative design of Vedic calculator using only few basic digital logic gates. Among the all sutras and sub sutras of ancient Vedic mathematics, the sutra ‘Urdhva Tiryagbyham’ is used here for square calculation of two bits numbers which results in an effortless and faster method of square calculation than all the existing methods. The design and minimization of the circuit has been carried out to achieve a standard architecture that is the simplest too. Here Xilinx ISE software tool is used rigorously to simulate the architecture.  

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