scholarly journals Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Li-Wei Ko ◽  
S. S. K. Ranga ◽  
Oleksii Komarov ◽  
Chung-Chiang Chen
Keyword(s):  
Motor Imagery ◽  
High Performance ◽  
Evoked Potential ◽  
Single Channel ◽  
Computer Interface ◽  
Multiple Channels ◽  
Eeg Signal ◽  
Feature Information ◽  
Improved Accuracy ◽  
Bci System

Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task.

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
M. K.M Rahman ◽  
Md. A. Mannan Joadder
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Quantitative Comparison ◽  
Computer Interface ◽  
Eeg Signal ◽  
Brain Signals ◽  
One Step ◽  
Overall Performance ◽  
Processing Steps ◽  
Bci System

Motor Imagery (MI) is a voluntary modulation of brain signals for specific action without real limb movement. It is essential to classify MI signal to design a brain computer interface (BCI). BCI involves a number of signal processing steps, and a lot of techniques have been developed for each step. There can be numerous combinations of these techniques at different steps that can be employed to design a BCI. This work focuses on MI-based BCI using EEG signal and reviews the existing techniques. More importantly, a detailed comparative study is performed to explore the important combinations of methods by comparing their performance quantitatively. Often a method, which performs very good in one combination, can be bad performer in other combinations and it is a dilemma for the researchers to select appropriate methods for their desired BCI application.In our performance analysis, we have systematically included the variations of methods in each step of BCI such that it gives idea to BCI researchers how each method in one step fits best with specific combinations of methods in other steps. We have shown that how much each step is sensitive towards overall performance of the BCI system.We hope that this work helps, especially for new researchers, to provide a better guideline for designing more efficient BCI system.

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.

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).

Designing Better, Cost-Effective Brain–Computer Interfaces

2015 ◽  
Vol 23 (4) ◽  
pp. 13-19 ◽  
Author(s):  
Chang S. Nam ◽  
Matthew Moore ◽  
Inchul Choi ◽  
Yueqing Li
Keyword(s):  
Steady State ◽  
Research And Development ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Cost Effective ◽  
Computer Interface ◽  
Computer Interfaces ◽  
The Brain ◽  
Bci System ◽  
Users With Special Needs

Despite the increase in research interest in the brain–computer interface (BCI), there remains a general lack of understanding of, and even inattention to, human factors/ergonomics (HF/E) issues in BCI research and development. The goal of this article is to raise awareness of the importance of HF/E involvement in the emerging field of BCI technology by providing HF/E researchers with a brief guide on how to design and implement a cost-effective, steady-state visually evoked potential (SSVEP)–based BCI system. We also discuss how SSVEP BCI systems can be improved to accommodate users with special needs.

DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

2017 ◽  
Vol 29 (03) ◽  
pp. 1750019 ◽  
Author(s):  
Malhar Pathak ◽  
A. K. Jayanthy
Keyword(s):  
Real Time ◽  
Single Channel ◽  
Detection System ◽  
Brain Computer Interface ◽  
Reference Electrode ◽  
Computer Interface ◽  
Processing Unit ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Interface System

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%.

scholarly journals Frequency Detection of Single Channel Steady State Visual Evoked Potential using Canonical Correlation Analysis

2019 ◽  
Vol 8 (3S2) ◽  
pp. 136-139
Keyword(s):  
Steady State ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Single Channel ◽  
Stimulus Frequency ◽  
Bci System ◽  
Visual Evoked

Wave generated into visual cortex of brain, when subject focused his/her attention on visual stimulus flickers at certain frequency. The main challenge with SSVEP Based Brain computer interface (BCI) System is to detect the stimulus frequency from recorded brain signal. Canonical Correlation analysis (CCA) is one of the most popular methods to recognize the frequency of Steady state visual evoked potential (SSVEP). This paper focuses on the study of CCA algorithm to recognize the SSVEP signal frequency. For experiment purpose, a single channel data with flickering frequency in the range of (6Hz-10Hz) is used. The performance of the BCI System is measured in terms of detection accuracy and Information transmission rate (ITR). The maximum accuracy is obtained as 83.90% and ITR is 15.35 at stimulus frequency of 8.2Hz

scholarly journals Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

2018 ◽  
Vol 7 (4) ◽  
pp. 2095 ◽  
Author(s):  
Tarmizi Ahmad Izzuddin ◽  
Norlaili Mat Safri ◽  
Fauzal Naim Zohedi ◽  
Mohamad Afzan Othman ◽  
Muhammad Shaufil Adha Shawkany Hazim
Keyword(s):  
Support Vector Machine ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Target Object ◽  
International Standards ◽  
Assistive Robotics ◽  
Computer Interface ◽  
Support Vector ◽  
Electroencephalogram Eeg ◽  
Bci System

Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.  

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