scholarly journals EEGG: An analytic brain-computer interface algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Computer Interface ◽  
Brain Regions ◽  
Poor Quality ◽  
Black Box ◽  
Interactive Effects ◽  
Computer Interface ◽  
Hand Movements ◽  
Eeg Data ◽  
The Brain ◽  
Interface Algorithm

<div><b>Objective.</b> A black box called brain-computer interface (BCI) model is used to identify another black box, the brain. However, one black box cannot explain another black box. This paper presents the first analytic "white box" brain-computer interface algorithm named EEGG. </div><div><b>Approach. </b>Independent and interactive effects of neurons or brain regions can fully describe the brain. This paper constructed a relationship model that extracted the independent and interactive features of EEG for intention recognition and analysis using ResDD, a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand movements and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, we explored EEGG's generalization ability according to cross-subject accuracy. Secondly, we transformed the EEGG model into a relationship spectrum expressing independent and interactive effects of brain regions. Then, the relationship spectrum was verified through the known ERD/ERS phenomenon. Finally, we explored the previously unreachable further analysis based on a BCI model.</div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the poor quality EEG data [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The transformed EEGG model showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that the interactive effects of brain regions put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation of the brain.</div><div><b>Significance.</b> EEGG implies that, henceforth, not only can BCI be used for recognition but also analysis.</div>

scholarly journals EEGG: An analytic brain-computer interface algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Computer Interface ◽  
Brain Regions ◽  
Poor Quality ◽  
Black Box ◽  
Interactive Effects ◽  
Computer Interface ◽  
Hand Movements ◽  
Eeg Data ◽  
The Brain ◽  
Interface Algorithm

<div><b>Objective.</b> A black box called brain-computer interface (BCI) model is used to identify another black box, the brain. However, one black box cannot explain another black box. This paper presents the first analytic "white box" brain-computer interface algorithm named EEGG. </div><div><b>Approach. </b>Independent and interactive effects of neurons or brain regions can fully describe the brain. This paper constructed a relationship model that extracted the independent and interactive features of EEG for intention recognition and analysis using ResDD, a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand movements and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, we explored EEGG's generalization ability according to cross-subject accuracy. Secondly, we transformed the EEGG model into a relationship spectrum expressing independent and interactive effects of brain regions. Then, the relationship spectrum was verified through the known ERD/ERS phenomenon. Finally, we explored the previously unreachable further analysis based on a BCI model.</div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the poor quality EEG data [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The transformed EEGG model showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that the interactive effects of brain regions put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation of the brain.</div><div><b>Significance.</b> EEGG implies that, henceforth, not only can BCI be used for recognition but also analysis.</div>

scholarly journals Brainwave assistive system for paralyzed individuals

2021 ◽  
Vol 2 (3) ◽  
pp. 79-89
Author(s):  
Md Ahnaf Shariar ◽  
Syeda Maliha Monowara ◽  
Md. Shafayat Ul Islam ◽  
Muhammed Junaid Noor Jawad ◽  
Saifur Rahman Sabuj
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Real Time ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cursor Control ◽  
Eeg Data ◽  
The Central Nervous System ◽  
The Brain ◽  
Real Time Application

The Brain-Computer Interface (BCI) is a system based on brainwaves that can be used to translate and comprehend the innumerable activities of the brain. Brainwave refers to the bioelectric impulses invariably produced in the human brain during neurotransmission, often measured as the action potential. Moreover, BCI essentially uses the widely studied Electroencephalography (EEG) technique to capture brainwave data. Paralysis generally occurs when there is a disturbance in the central nervous system prompted by a neurodegenerative or unforeseen event. To overcome the obstacles associated with paralysis, this paper on the brainwave-assistive system is based on the BCI incorporated with Internet-of-things. BCI can be implemented to achieve control over external devices and applications. For instance, the process of cursor control, motor control, neuroprosthetics and wheelchair control, etc. In this paper, the OpenBCI Cyton-biosensing board has been used for the collection of the EEG data. The accumulated EEG data is executed subsequently to obtain control over the respective systems in real-time. Hence, it can be concluded that the experiments of the paper support the idea of controlling an interfaced system through the real-time application of EEG data.

scholarly journals Electroencephalogram with Machine Learning for Estimation of Mental Confusion Level

2019 ◽  
Vol 9 (2) ◽  
pp. 761-765
Keyword(s):  
Machine Learning ◽  
Concentration Level ◽  
Brain Computer Interface ◽  
Quantitative Measure ◽  
Threshold Value ◽  
Machine Learning Algorithms ◽  
Computer Interface ◽  
Eeg Data ◽  
The Brain ◽  
Bci System

Estimating the mental state of an individual is crucial to many applications. A quantitative measure of the confusion one faces while doing a task can be useful in determining which subtask is the most difficult. This paper thus aims to develop an algorithm to estimate the confusion score using EEG signals collected using a Neurosky Mindwave Headset. Also, a full contextual audio based confusion score is generated to improve the system's resilience. In this paper, the final algorithm is used to propose an EEG based system to enable the UI/UX testing which can help in confusion estimation and thus provide a qualitative means to measure the attention and concentration level of people which can be extended to various applications. The raw EEG data collected from the device was used to calculate the confusion score using various Machine Learning algorithms. This brain computer interface (BCI) system can be extended for calculating the confusion score of a person which can be used for various applications such as teaching, child health monitoring, suicide prevention, mental health analysis etc. The brain computer interface thus calculates the confusion score and based on the threshold value of the attention and concentration level it performs certain actions such as sending messages and alerts to emergency contacts. This is further extended to solve the problem of Usability testing in Human Computer Interaction.

scholarly journals EEGG: An analytic brain-computer interface algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Regions ◽  
Poor Quality ◽  
Classification Performance ◽  
Black Box ◽  
Data Driven ◽  
Left Hand ◽  
Convolutional Networks ◽  
Data Driven Approach ◽  
Traditional Sense ◽  
The Brain

<div><div><b>Objective.</b> In the traditional sense, the modeling approaches can be divided into white-box (physics-based), black-box (data-driven), and gray-box (the combination of physics-based and data-driven). Because the human brain is a black box itself, the EEG-BCI algorithm is generally a data-driven approach. It generates a black-box or gray-box (e.g., "Visualizing convolutional networks") model. However, one black- or gray-box cannot completely explain the brain. This paper presents the first analytic "white-box" EEG-BCI algorithm using Gang neurons (EEGG).</div><div><br></div><div><b>Approach.</b> Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance according to cross-subject accuracy. Secondly, this paper transformed the EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified through the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of brain.</div><div><br></div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the data of poor quality [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that brain regions' interactive components put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation in the brain.</div><div><br></div><div><b>Significance.</b> EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting Taylor series, rather than the fuzzy interpretation of outputs, which offers a novel frame for analysis of the brain.</div></div><div><p></p></div>

scholarly journals EEGG: An Analytic Brain-computer Interface Algorithm

2021 ◽  
Author(s):  
Gang Liu ◽  
Jing Wang
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Classification Performance ◽  
Data Driven ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Data Driven Approach ◽  
Left And Right ◽  
The Brain

<div><div> <p><a></a></p><div> <p><a></a><a><i>Objective. </i></a>Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. </p> <p><i>Approach. </i>Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG’s classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. </p> <p><i>Main results. </i>(1) EEGG was more robust than typical “CSP+” algorithms for the poorquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. </p> <p><i>Significance. </i>EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (<i>in analogy with the data-driven but human-readable Fourier transform and frequency spectrum</i>), which offers a novel frame for analysis of the brain.</p> </div> </div></div><div><p></p></div>

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.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

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.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

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