scholarly journals Feature extraction from EEG signals and regularization for brain-computer interface

2019 ◽  
Author(s):  
◽  
Vasilisa Mishuhina
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals

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 Investigation of EEG Signal Classification Techniques for Brain Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 3266-3274
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Classification Techniques ◽  
Feature Extraction And Selection ◽  
Bci System

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

scholarly journals Processing EEG signals acquired from a consumer grade BCI device

2018 ◽  
Vol 11 (2) ◽  
pp. 29-34 ◽  
Author(s):  
Fanny Monori ◽  
Stefan Oniga
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Human Brain ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Multiclass Classification ◽  
Computer Interface ◽  
Clinical Settings ◽  
Eeg Signals ◽  
Complex Information

Abstract BCI (Brain-Computer Interface) is a technology which goal is to create and manage a connection between the human brain and a computer with the help of EEG signals. In the last decade consumer-grade BCI devices became available thus giving opportunity to develop BCI applications outside of clinical settings. In this paper we use a device called NeuroSky MindWave Mobile. We investigate what type of information can be deducted from the data acquired from this device, and we evaluate whether it can help us in BCI applications. Our methods of processing the data involves feature extraction methods, and neural networks. Specifically, we make experiments with finding patterns in the data by binary and multiclass classification. With these methods we could detect sharp changes in the signal such as blinking patterns, but we could not extract more complex information successfully.

Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

2015 ◽  
Vol 25 (14) ◽  
pp. 1540023
Author(s):  
Germán Rodríguez-Bermúdez ◽  
Miguel Ángel Sánchez-Granero ◽  
Pedro J. García-Laencina ◽  
Manuel Fernández-Martínez ◽  
José Serna ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Information Acquisition ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Self Similarity ◽  
Self Similar ◽  
Bci System

A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

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.

scholarly journals A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106826
Author(s):  
Giovanni Acampora ◽  
Pasquale Trinchese ◽  
Autilia Vitiello
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals
Sign in / Sign up

Export Citation Format

Share Document