Investigation on EEG Features and Classification Methods for Brain Computer Interface

2017 ◽  
pp. 729-735 ◽  
Author(s):  
Huynh Tri Quang ◽  
Nguyen Thanh Chau ◽  
Huynh Nhu Ngoc Hien ◽  
Nguyen Duc Thang ◽  
Toi Vo Van
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Methods ◽  
Eeg Features

Analysis of EEG Features for Brain Computer Interface Application

2020 ◽  
pp. 529-540 ◽  
Author(s):  
Mamunur Rashid ◽  
Norizam Sulaiman ◽  
Mahfuzah Mustafa ◽  
Mohd Shawal Jadin ◽  
Muhd Sharfi Najib ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Features

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Nikolay V. Manyakov ◽  
Nikolay Chumerin ◽  
Adrien Combaz ◽  
Marc M. Van Hulle
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Methods ◽  
Linear Classifier ◽  
Typing System ◽  
The Individual ◽  
Lateral Sclerosis ◽  
Selection Of

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

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