scholarly journals Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

2019 ◽  
Vol 9 (23) ◽  
pp. 4990 ◽  
Author(s):  
Jusas ◽  
Samuvel
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Extraction Process ◽  
Computer Interface ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Fisher Ratio ◽  
Frequency Domains ◽  
Subject Specific ◽  
The Time Domain

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher′s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher′s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

scholarly journals Hjorth features and k-nearest neighbors algorithm for visual imagery classification

2021 ◽  
Author(s):  
Fabio Ricardo Llorella Costa ◽  
Gustavo Patow
Keyword(s):  
Time Domain ◽  
Visual Imagery ◽  
Brain Computer Interface ◽  
Nearest Neighbors ◽  
Computer Interface ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Kappa Value ◽  
The Time Domain

Abstract Visual imagery is an interesting paradigm for use in Brain-Computer Interface systems. Through visual imagery we can extend the potential of BCI systems beyond motor imagery or evoked potentials. In this work we have studied the possibility of classifying different visual imagery shapes in the time domain using EEG signals, with the Hjorth parameters and k-nearest neighbors classifier 69% accuracy has been obtained with a Cohen's kappa value of 0.64 in the classification of seven geometric shapes, obtaining results superior to other related works.

Effect of Time and Frequency Manipulation on Syllable Perception in Developmental Dyslexics

1997 ◽  
Vol 40 (4) ◽  
pp. 912-924 ◽  
Author(s):  
Ken I. McAnally ◽  
Peter C. Hansen ◽  
Piers L. Cornelissen ◽  
John F. Stein
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Developmental Dyslexia ◽  
Stop Consonant ◽  
Stop Consonants ◽  
Systematic Improvement ◽  
Frequency Domains ◽  
The Time Domain ◽  
Time And Frequency Domains

Many people with developmental dyslexia have difficulty perceiving stop consonant contrasts as effectively as other people and it has been suggested that this may be due to perceptual limitations of a temporal nature. Accordingly, we predicted that perception of such stimuli by listeners with dyslexia might be improved by stretching them in time—equivalent to speaking slowly. Conversely, their perception of the same stimuli ought to be made even worse by compressing them in time—equivalent to speaking quickly. We tested 15 children with dyslexia on their ability to identify correctly consonant-vowel-consonant (CVC) stimuli that had been stretched or compressed in the time domain. We also tested their perception of the same CVC stimuli after the formant transitions had been stretched or compressed in the frequency domain. Contrary to our predictions, we failed to find any systematic improvement in their performance with either manipulation. We conclude that simple manipulations in the time and frequency domains are unlikely to benefit the ability of people with dyslexia to discriminate between CVCs containing stop consonants.

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.

A CFD APPROACH TO THE COMPUTATION OF THE ACOUSTIC RESPONSE OF EXHAUST MUFFLERS

2005 ◽  
Vol 13 (02) ◽  
pp. 301-316 ◽  
Author(s):  
A. BROATCH ◽  
X. MARGOT ◽  
A. GIL ◽  
F. D. DENIA
Keyword(s):  
Numerical Solution ◽  
Time Domain ◽  
Cfd Simulation ◽  
Transmission Loss ◽  
Three Dimensional ◽  
Alternative Procedure ◽  
Acoustic Response ◽  
Frequency Domains ◽  
The Time Domain ◽  
Numerical Approaches

The study of the three-dimensional acoustic field inside an exhaust muffler is usually performed through the numerical solution of the linearized equations. In this paper, an alternative procedure is proposed, in which the full equations are solved in the time domain. The procedure is based on the CFD simulation of an impulsive test, so that the transmission loss may be computed and compared with measurements and other numerical approaches. Also, the details of the flow inside the muffler may be studied, both in the time and the frequency domains. The results obtained compare favorably with a conventional FEM calculation, mostly in the ability of the procedure to account for dissipative processes inside the muffler.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

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 Asymptomatic Severe Vagal and Sympathetic Cardiac Denervation in Holmes-Adie’s Syndrome

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
B. Estañol ◽  
R. C. Callejas-Rojas ◽  
S. Cortés ◽  
R. Martínez-Memije ◽  
O. Infante-Vázquez ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Blood Pressure Variability ◽  
Standing Position ◽  
Cardiac Denervation ◽  
Frequency Domains ◽  
Systolic Blood Pressure Variability ◽  
Adie's Syndrome ◽  
The Time Domain ◽  
Time And Frequency Domains

A 40-year-old woman was found to have bilateral Adie’s pupils and generalized muscle stretch areflexia. She did not have orthostatic hypotension but, in an ECG strip in the office, she appeared to have an almost fixed heart rate. We thus studied the heart rate variability (HRV) and the systolic blood pressure variability (SBPV) in supine and standing position and also during rhythmic breathing. We found a decreased HRV in the time domain with very low standard deviation in supine and standing position and during rhythmic breathing. HRV in the frequency domain was low with a decrease in the absolute power of HF and LF and a decrease in the sympathovagal balance in supine and standing positions. SBPV in the time and frequency domains was found to be normal. This patient with Holmes-Adie syndrome had an asymptomatic severe loss of HRV and a preserved SBPV. The global decrease in the HRV in the time and frequency domains indicated that she had both vagal and sympathetic cardiac denervation, whereas the preserved SBPV suggested normal innervation of the blood vessels.

scholarly journals Electrophysiological Analysis of Agony and Consciousness in Comatose

2019 ◽  
Vol 8 (6) ◽  
pp. 4256-4259
Keyword(s):  
Feedback System ◽  
Human Interaction ◽  
Computer Interface ◽  
Brain State ◽  
Eeg Signals ◽  
Digital World ◽  
Brain States ◽  
The Time Domain ◽  
Direct Feedback ◽  
Unconventional Method

An Interface is developed between human brain and a digital world, called as brain-computer interface (BCI). In various applications, BCI is used nowadays in our day to day activities. The recent researches focus on the BCI communication for coma patients for their thought related activities. BCI is an unconventional method to ordinary communication and direct feedback system. Due to the presence of neural relations, there will be an existence of different rhythms for different brain states. In consistent, the rhythms produces a different waves portrayed by different amplitudes and frequencies. This proposed work deals with the different brain state analysis by the Electroencephalography (EEG) signals due to the neuronal reactions. EEG signal is acquired from the brainwave sensor and the signals are detached from the various noises. The time domain features are extracted in terms of various frequency ranges and the respective commands are classified for analyzing the state of the coma patients. The proposed work is analyzed in the software as it involves human interaction

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