An Auditory Brain-Computer Interface Based on Dichotic Listening Paradigm

This study proposes a novel auditory brain–computer interface (BCI) based on dichotic listening paradigm, which allows the subject to select a target from two different sound stimulus sequences played in each ear. EEG data from 6 subjects has shown that the amplitude of N200 and P300 elicited by target was significantly higher than that of non-target ones. We found the N2ac component: a negativity wave in the N2 latency range at anterior contralateral electrodes. The target detection accuracy was assessed by support vector machine (SVM). The accuracy based on multiple electrodes is higher than a single electrode. The dichotic listening paradigm can be used for binary-class of brain-computer interface system.

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Practical Artifact Removal Brain-Computer Interface System

Advances in Bioinformatics and Biomedical Engineering - Emerging Theory and Practice in Neuroprosthetics ◽

10.4018/978-1-4666-6094-6.ch013 ◽

2014 ◽

pp. 265-277

Author(s):

Wei-Yen Hsu

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Support Vector ◽

Artifact Removal ◽

Eeg Data ◽

Interface System ◽

Electroencephalogram Eeg ◽

Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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A Robust P300-Speller Brain-Computer Interface Based on Kernel PCA

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.300-301.721 ◽

2013 ◽

Vol 300-301 ◽

pp. 721-724 ◽

Cited By ~ 2

Author(s):

Yi Hung Liu ◽

Jui Tsung Weng ◽

Han Pang Huang ◽

Jyh Tong Teng

Keyword(s):

Brain Activity ◽

Brain Computer Interface ◽

Principal Component ◽

Computer Interface ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Detection Accuracy ◽

Motor Disabilities ◽

P300 Speller ◽

Severe Motor

P300 speller is a well-known brain-computer interface (BCI), which allows patients with severe motor disabilities to spell words through the recognition on patients’ brain activity measured by electroencephalography (EEG). The brain-activity recognition is essentially a task of detecting of P300 responses in EEG signals. Support vector machine (SVM) has been a widely-used P300 detector in existing works. However, SVM is computationally expensive, greatly reducing the usability of the speller BCI for practical use. To address this issue, we propose in this paper a novel P300 detector, which is based on the kernel principal component analysis (KPCA). The proposed detector has a lower computational complexity, and can measure the belongingness of an input EEG to P300 class by the construction of EEG in nonlinear eigenspaces. Results carried out on subjects show that the proposed method is able to significantly shorten offline training sessions of the speller BCI while achieving high online P300-detection accuracy.

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Artefacts Removal to Detect Visual Evoked Potentials in Brain Computer Interface Systems

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.41.91 ◽

2019 ◽

Vol 41 ◽

pp. 91-103 ◽

Cited By ~ 2

Author(s):

Hamidreza Abbaspour ◽

Nasser Mehrshad ◽

Seyyed Mohammad Razavi ◽

Luca Mesin

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Support Vector ◽

Automated Method ◽

Processing Step ◽

Eeg Data ◽

Potential Applications ◽

Scalp Electroencephalogram ◽

Electrode Connection ◽

Visual Evoked

The interference of artefacts with evoked scalp electroencephalogram (EEG) responses is a problem in event related brain computer interface (BCI) system that reduces signal quality and interpretability of user's intentions. Many strategies have been proposed to reduce the effects of non-neural artefacts, while the activity of neural sources that do not reflect the considered stimulation has been neglected. However discerning such activities from those to be retained is important, but subtle and difficult as most of their features are the same. We propose an automated method based on a combination of a genetic algorithm (GA) and a support vector machine (SVM) to select only the sources of interest. Temporal, spectral, wavelet, autoregressive and spatial properties of independent components (ICs) of EEG are inspected. The method selects the most distinguishing subset of features among this comprehensive fused set of information and identifies the components to be preserved. EEG data were recorded from 12 healthy subjects in a visual evoked potential (VEP) based BCI paradigm and the corresponding ICs were classified by experts to train and test the algorithm. They were contaminated with different sources of artefacts, including electromyogram (EMG), electrode connection problems, blinks and electrocardiogram (ECG), together with neural contributions not related to VEPs. The accuracy of ICs classification was about 88.5% and the energetic residual error in recovering the clean signals was 3%. These performances indicate that this automated method can effectively identify and remove main artefacts derived from either neural or non-neural sources while preserving VEPs. This could have important potential applications, contributing to speed and remove subjectivity of the cleaning procedure by experts. Moreover, it could be included in a real time BCI as a pre-processing step before the identification of the user’s intention.

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Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning

10.1101/389395 ◽

2018 ◽

Author(s):

Tanja Krumpe ◽

Christian Scharinger ◽

Wolfgang Rosenstiel ◽

Peter Gerjets ◽

Martin Spüler

Keyword(s):

Machine Learning ◽

Working Memory ◽

Executive Functions ◽

Memory Load ◽

Brain Computer Interface ◽

Theoretical Models ◽

Computer Interface ◽

Support Vector ◽

Learning Approaches ◽

Eeg Data

AbstractObjectiveAccording to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches.MethodsAn existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroenzephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis.ResultsThe SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power.SignificanceIn this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.

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A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI

Applied Sciences ◽

10.3390/app11020674 ◽

2021 ◽

Vol 11 (2) ◽

pp. 674

Author(s):

Marianna Koctúrová ◽

Jozef Juhár

Keyword(s):

Visual Stimuli ◽

Computer Interface ◽

Single Subject ◽

Detection Accuracy ◽

The Novel ◽

Activity Detection ◽

Speech Activity ◽

Novel Approach ◽

Eeg Data ◽

Speech Activity Detection

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

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

ACM Transactions on Management Information Systems ◽

10.1145/3458791 ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.310.660 ◽

2013 ◽

Vol 310 ◽

pp. 660-664 ◽

Cited By ~ 1

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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