scholarly journals COMPLEXITY AND INFORMATION-BASED ANALYSIS OF THE ELECTROENCEPHALOGRAM (EEG) SIGNALS IN STANDING, WALKING, AND WALKING WITH A BRAIN–COMPUTER INTERFACE

Fractals ◽  
2021 ◽  
Author(s):  
JANARTHANAN RAMADOSS ◽  
NORAZRYANA MAT DAWI ◽  
KARTHIKEYAN RAJAGOPAL ◽  
HAMIDREZA NAMAZI
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Brain Computer Interface ◽  
Fractal Theory ◽  
Brain Activation ◽  
Computer Interface ◽  
Complex Structures ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

In this paper, we analyzed the variations in brain activation between different activities. Since Electroencephalogram (EEG) signals as an indicator of brain activation contain information and have complex structures, we employed complexity and information-based analysis. Specifically, we used fractal theory and Shannon entropy for our analysis. Eight subjects performed three different activities (standing, walking, and walking with a brain–computer interface) while their EEG signals were recorded. Based on the results, the complexity and information content of EEG signals have the greatest and smallest values in walking and standing, respectively. Complexity and information-based analysis can be applied to analyze the activations of other organs in different conditions.

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

2019 ◽  
pp. 241-251
Author(s):  
Subrota Mazumdar ◽  
Rohit Chaudhary ◽  
Suruchi Suruchi ◽  
Suman Mohanty ◽  
Divya Kumari ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Nearest Neighbor ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Test Cases ◽  
Multiple Channels ◽  
Eeg Signals ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

scholarly journals Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Dongrui Wu ◽  
Lieyun Ding ◽  
Hanbin Luo ◽  
Chin-Teng Lin ◽  
...  
Keyword(s):  
Visual Evoked Potential ◽  
Brain Computer Interface ◽  
Effective Means ◽  
Computer Interface ◽  
Eeg Signals ◽  
Input Text ◽  
User Frustration ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Lateral Sclerosis

Abstract An electroencephalogram (EEG)-based brain–computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

scholarly journals Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1273
Author(s):  
She ◽  
Zhou ◽  
Gan ◽  
Ma ◽  
Luo
Keyword(s):  
Brain Computer Interface ◽  
Label Propagation ◽  
Learning System ◽  
Computer Interface ◽  
Eeg Signals ◽  
Supervised Methods ◽  
Comparison Algorithms ◽  
Electroencephalogram Eeg ◽  
Graph Label

In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.

scholarly journals Automated Artifact Removal in EEG Signals of Brain Computer Interface using Wavelets and ICA

2019 ◽  
Vol 8 (12) ◽  
pp. 896-909
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Control Line ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Natural Sources ◽  
Control Signals ◽  
The Mind ◽  
Electroencephalogram Eeg ◽  
The Brain

A brain-computer interface (BCI) gives a correspondence channel that interconnects the mind with an outside device. The most generally utilized system for getting BCI control signals from the brain is the electroencephalogram (EEG). In the proposed paper, BCI framework towards an EEG chronicles are reviewed into and found that the expansion of a counterfeit motion toward it, which is brought about by eye flickers, eye development, muscle and cardiovascular commotion, just as non-natural sources (e.g., control line clamor). According to the writing survey it is discovered that these issues can be overwhelmed by utilizing mix of wavelet deterioration, independent component analysis (ICA), and thresholding

scholarly journals A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

2021 ◽  
Vol 15 ◽  
Author(s):  
Xin Huang ◽  
Yilu Xu ◽  
Jing Hua ◽  
Wenlong Yi ◽  
Hua Yin ◽  
...  
Keyword(s):  
Signal Processing ◽  
Electronic Device ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Eeg Signals ◽  
Good Classification Performance ◽  
Calibration Time ◽  
Electroencephalogram Eeg ◽  
Target Subject

In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

scholarly journals Kombinasi Sinyal EEG dan Giroskop untuk Kendali Mobil Virtual dengan Menggunakan Modifikasi ICA dan SVM

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Ahmad Reza Musthafa ◽  
Handayani Tjandrasa
Keyword(s):  
Support Vector Machine ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Signal Classification ◽  
Electroencephalogram Eeg

Abstract. Electroencephalogram (EEG) signals has been widely researched and developed in many fields of science. EEG signals could be classified into useful information for the application of Brain Computer Interface topic (BCI). In this research, we focus in a topic about driving a car using EEG signal. There are many approaches in EEG signal classification, but some approaches do not robust EEG signals that have many artifacts and have been recorded in real time. This research aims to classify EEG signals to obtain more optimal results, especially EEG signals with many artifacts and can be recorded in realtime. This research uses Emotiv EPOC device to record EEG signals in realtime. In this research, we propose the combination of Automatic Artifact Removal (AAR) and Support Vector Machine (SVM) which has 71% of accuracy that can be applied to drive a virtual car.Keyword: EEG signal classification, automatic artifact removal, brain computer interface Abstrak. Penelitian berbasis sinyal Electroencephalogram (EEG) telah banyak diteliti dan dikembangkan pada berbagai bidang ilmu pengetahuan. Sinyal EEG dapat diklasifikasikan ke dalam bentuk informasi untuk pengaplikasian topik Brain Computer Interface (BCI). Pada penelitian ini difokuskan pada topik pengendalian mobil menggunakan perintah sinyal EEG. Terdapat beberapa pendekatan dalam klasifikasi sinyal EEG, tetapi beberapa pendekatan tersebut tidak robust terhadap sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini bertujuan untuk mengklasifikasikan sinyal EEG dengan hasil lebih optimal, khususnya pada sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini menggunakan perangkat Emotiv EPOC untuk merekam sinyal EEG secara realtime. Pada penelitian ini diusulkan kombinasi Automatic Artifact Removal (AAR) dan Support Vector Machine (SVM) yang menghasilkan hasil akurasi sebesar 71% untuk klasifikasi sinyal EEG pada kasus pengendalian mobil virtual.Kata Kunci: EEG signal classification, automatic artifact removal, brain computer interface

scholarly journals Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1315
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Brain Computer Interface ◽  
Recognition Rate ◽  
Computational Cost ◽  
Component Analysis ◽  
Classification Performance ◽  
Computer Interface ◽  
Eeg Signals ◽  
Spatial Filters ◽  
Improved Performance ◽  
Electroencephalogram Eeg

Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.

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