MATHEMATICAL MODEL OF BRAIN-COMPUTER INTERFACE BASED ON THE ANALYSIS OF P300 EVENT RELATED POTENTIALS

2021 ◽  
pp. 48-67
Author(s):  
Sergey A. Obukhov ◽  
◽  
Valery P. Stepanov ◽  
Igor V. Rudakov ◽  
◽  
...  
Keyword(s):  
Mathematical Model ◽  
Evoked Potentials ◽  
Event Related Potentials ◽  
Support Vector ◽  
Human Machine Interaction ◽  
Vector Method ◽  
External Stimulation ◽  
Related Potentials ◽  
Eeg Recordings ◽  
Machine Interaction

The evoked potentials (EP) method consists in recording bioelectric reactions of the brain in response to external stimulation or while performing cognitive tasks. The goal of the work is to develop a mathematical model of the system for detection and classification of evoked potentials on the electroencephalogram (EEG). The main odd of the machine EP detection are artifacts from EEG recordings and the high variability of potentials. EP detection and classification algorithm includes three stages. At the preliminary stage, the frequency-time and spatial signal transformations – a set of Butterworth frequency filters, linear composition and averaging of the recorded signals from different sensors are used to remove noise and uninformative EEG components. The next step is the direct fixation and averaging of the evoked potentials. At the final stage, to reduce the dimension of the problem, the information features vector is formed. The parameterized image is used as input of the binary classifier. The support vector method is used to construct the classifier. During the study, the optimization of the regularization C parameter of the classifier was carried out using the estimation of sliding control. The proposed solution is useful for human-machine interaction and for medical procedures with biofeedback.

scholarly journals Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Sangin Park ◽  
Jihyeon Ha ◽  
Laehyun Kim
Keyword(s):  
Evoked Potentials ◽  
Mental Arithmetic ◽  
Mental Workload ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Support Vector ◽  
Arithmetic Task ◽  
Related Potentials ◽  
Mental Arithmetic Task

The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERPHEP, containing the heartbeat in a period of 280–700ms in ERP epochs after the target; ERPA-HEP, not including the heartbeat within the same period; and ERPT, all trials including ERPA-HEP and ERPHEP. We then compared MWL classification performance using the amplitude and latency of the P600 ERP among the three conditions. The ERPA-HEP condition achieved an accuracy of 100% using a radial basis function-support vector machine (with 10-fold cross-validation), showing an increase of 14.3 and 28.6% in accuracy compared to ERPT (85.7%) and ERPHEP (71.4%), respectively. The results suggest that evoked potentials caused by heartbeat overlapped or interfered with the ERPs and weakened the ERP response to stimuli. This study reveals the effect of the evoked potentials induced by heartbeats on the performance of the MWL classification based on ERPs.

scholarly journals Potenciais evocados auditivos corticais no transtorno do espectro do autismo: revisão sistemática

CoDAS ◽  
2021 ◽  
Vol 33 (2) ◽  
Author(s):  
Mariana Keiko Kamita ◽  
Liliane Aparecida Fagundes Silva ◽  
Carla Gentile Matas
Keyword(s):  
Autism Spectrum Disorder ◽  
Evoked Potentials ◽  
Autistic Disorder ◽  
Web Of Science ◽  
Event Related Potentials ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Cochrane Library ◽  
Related Potentials

RESUMO Objetivo Identificar e analisar quais são os achados característicos dos Potenciais Evocados Auditivos Corticais (PEAC) em crianças e/ou adolescentes com Transtorno do Espectro do Autismo (TEA) em comparação do desenvolvimento típico, por meio de uma revisão sistemática da literatura. Estratégia de pesquisa Após formulação da pergunta de pesquisa, foi realizada uma revisão da literatura em sete bases de dados (Web of Science, Pubmed, Cochrane Library, Lilacs, Scielo, Science Direct, e Google acadêmico), com os seguintes descritores: transtorno do espectro autista (autism spectrum disorder), transtorno autístico (autistic disorder), potenciais evocados auditivos (evoked potentials, auditory), potencial evocado P300 (event related potentials, P300) e criança (child). A presente revisão foi cadastrada no Próspero, sob número 118751. Critérios de seleção Foram selecionados estudos publicados na integra, sem limitação de idioma, entre 2007 e 2019. Análise dos dados: Foram analisadas as características de latência e amplitude dos componentes P1, N1, P2, N2 e P3 presentes nos PEAC. Resultados Foram localizados 193 estudos; contudo 15 estudos contemplaram os critérios de inclusão. Embora não tenha sido possível identificar um padrão de resposta para os componentes P1, N1, P2, N2 e P3, os resultados da maioria dos estudos demonstraram que indivíduos com TEA podem apresentar diminuição de amplitude e aumento de latência do componente P3. Conclusão Indivíduos com TEA podem apresentar respostas diversas para os componentes dos PEAC, sendo que a diminuição de amplitude e aumento de latência do componente P3 foram as características mais comuns.

scholarly journals Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021 ◽  
Vol 11 (23) ◽  
pp. 11252
Author(s):  
Ayana Mussabayeva ◽  
Prashant Kumar Jamwal ◽  
Muhammad Tahir Akhtar
Keyword(s):  
Healthy Subjects ◽  
Single Channel ◽  
Event Related Potentials ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Related Potentials ◽  
Als Patients

Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.

scholarly journals The Potential of Trial-by-Trial Variabilities of Ongoing-EEG, Evoked Potentials, Event Related Potentials and fMRI as Diagnostic Markers for Neuropsychiatric Disorders

2019 ◽  
Vol 12 ◽  
Author(s):  
Carlos Trenado ◽  
Anaí González-Ramírez ◽  
Victoria Lizárraga-Cortés ◽  
Nicole Pedroarena Leal ◽  
Elias Manjarrez ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Event Related Potentials ◽  
Neuropsychiatric Disorders ◽  
Diagnostic Markers ◽  
Related Potentials ◽  
Ongoing Eeg

scholarly journals SCENE AND OBJECT CLASSIFICATION USING BRAIN WAVES SIGNAL

2017 ◽  
Vol 10 (13) ◽  
pp. 137
Author(s):  
Darshan A Khade ◽  
Ilakiyaselvan N
Keyword(s):  
Artificial Intelligence ◽  
Event Related Potentials ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Eeg Signals ◽  
Future Technology ◽  
Brain Waves ◽  
Brain Signals ◽  
Related Potentials ◽  
Signal Machine

This study aims to classify the scene and object using brain waves signal. The dataset captured by the electroencephalograph (EEG) device by placing the electrodes on scalp to measure brain signals are used. Using captured EEG dataset, classifying the scene and object by decoding the changes in the EEG signals. In this study, independent component analysis, event-related potentials, and grand mean are used to analyze the signal. Machine learning algorithms such as decision tree, random forest, and support vector machine are used to classify the data. This technique is useful in forensic as well as in artificial intelligence for developing future technology. 

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