scholarly journals Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction

2021 ◽  
Vol 15 ◽  
Author(s):  
Feifei Qi ◽  
Wenlong Wang ◽  
Xiaofeng Xie ◽  
Zhenghui Gu ◽  
Zhu Liang Yu ◽  
...  
Keyword(s):  
Wavelet Decomposition ◽  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Spatial Filtering ◽  
Classification Performance ◽  
Single Trial ◽  
Eeg Signals ◽  
Orthogonal Wavelet ◽  
Spatial Filters ◽  
Eeg Classification

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

scholarly journals Modulation Classification of MFSK Modulated Signals Using Wavelet Decomposition

2018 ◽  
pp. 36-40
Author(s):  
Burcu Baris ◽  
Damla Gurkan Kuntalp ◽  
Mehmet Emre Cek
Keyword(s):  
Carrier Frequency ◽  
Communication Systems ◽  
Wavelet Decomposition ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Classification Performance ◽  
Discrete Wavelet ◽  
Modulation Classification ◽  
Frequency Range ◽  
Intelligent Communication

In this study,a wavelet decomposition based method is proposed for determining the modulation type of the incoming signal to the receiver which is one of the important problems in intelligent communication systems. In this method, it is aimed to design the transmitted signal for determining the type of Mary FSK modulated signal and to detect the energy in each frequency band by using Discrete Wavelet Transform (DWT). For this, standard deviations in the lower bands are as features. In order to evaluate the performance of the classifier, simulation studies have been performed at different signal-to-noise ratio (SNR) levels. When the results for different frequency settings, i.e. carrier frequency and frequency range, it is seen that the classifier using the K-means clustering algorithm has a higher correct classification performance than the results reported in the literature when the suitable carrier frequency and frequency range are selected.

scholarly journals Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective

2021 ◽  
Vol 11 (11) ◽  
pp. 1424
Author(s):  
Yuhong Zhang ◽  
Yuan Liao ◽  
Yudi Zhang ◽  
Liya Huang
Keyword(s):  
Classification Accuracy ◽  
Active Regions ◽  
Classification Performance ◽  
Sensor Data ◽  
Single Trial ◽  
Eeg Signals ◽  
Emergency Braking ◽  
Node Importance ◽  
Novel Approach ◽  
Eeg Features

In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm–Feature selection–Classification System (KFCS)is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.

scholarly journals Direction-of-Arrival Estimation for Coherent Sources via Sparse Bayesian Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang-Meng Liu ◽  
Zheng Liu ◽  
Dao-Wang Feng ◽  
Zhi-Tao Huang
Keyword(s):  
Bayesian Learning ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Relevance Vector Machine ◽  
Sparse Bayesian Learning ◽  
Spatial Filters ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatial Sparsity ◽  
Array Geometry

A spatial filtering-based relevance vector machine (RVM) is proposed in this paper to separate coherent sources and estimate their directions-of-arrival (DOA), with the filter parameters and DOA estimates initialized and refined via sparse Bayesian learning. The RVM is used to exploit the spatial sparsity of the incident signals and gain improved adaptability to much demanding scenarios, such as low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources, and the spatial filters are introduced to enhance global convergence of the original RVM in the case of coherent sources. The proposed method adapts to arbitrary array geometry, and simulation results show that it surpasses the existing methods in DOA estimation performance.

A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs

2020 ◽  
Vol 30 (05) ◽  
pp. 2050020 ◽  
Author(s):  
Qingguo Wei ◽  
Shan Zhu ◽  
Yijun Wang ◽  
Xiaorong Gao ◽  
Hai Guo ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Signal To Noise Ratio ◽  
Classification Performance ◽  
Training Data ◽  
Data Driven ◽  
Spatial Filters ◽  
Data Set ◽  
Practical Applications

Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.

Designing optimal spatial filters for single-trial EEG classification in a movement task

1999 ◽  
Vol 110 (5) ◽  
pp. 787-798 ◽  
Author(s):  
Johannes Müller-Gerking ◽  
Gert Pfurtscheller ◽  
Henrik Flyvbjerg
Keyword(s):  
Single Trial ◽  
Spatial Filters ◽  
Eeg Classification

scholarly journals Comparison of linear spatial filters for identifying oscillatory activity in multichannel data

2016 ◽  
Author(s):  
Michael X Cohen
Keyword(s):  
Large Scale ◽  
Signal To Noise Ratio ◽  
Spatial Filtering ◽  
Spatial Filter ◽  
Oscillatory Activity ◽  
Signal To Noise ◽  
Spatial Filters ◽  
Electrical Fields ◽  
Noise Ratio ◽  
Filtering Techniques

AbstractBackgroundLarge-scale synchronous neural activity produces electrical fields that can be measured by electrodes outside the head, and volume conduction ensures that neural sources can be measured by many electrodes. However, most data analyses in M/EEG research are univariate, meaning each electrode is considered as a separate measurement. Several multivariate linear spatial filtering techniques have been introduced to the cognitive electrophysiology literature, but these techniques are not commonly used; comparisons across filters would be beneficial to the field.New methodThe purpose of this paper is to evaluate and compare the performance of several linear spatial filtering techniques, with a focus on those that use generalized eigendecomposition to facilitate dimensionality reduction and signal-to-noise ratio maximization.ResultsSimulated and empirical data were used to assess the accuracy, signal-to-noise ratio, and interpretability of the spatial filter results. When the simulated signal is powerful, different spatial filters provide convergent results. However, more subtle signals require carefully selected analysis parameters to obtain optimal results.Comparison with existing methodsLinear spatial filters can be powerful data analysis tools in cognitive electrophysiology, and should be applied more often; on the other hand, spatial filters can latch onto artifacts or produce uninterpretable results.ConclusionsHypothesis-driven analyses, careful data inspection, and appropriate parameter selection are necessary to obtain high-quality results when using spatial filters.

scholarly journals Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1315
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Brain Computer Interface ◽  
Recognition Rate ◽  
Computational Cost ◽  
Component Analysis ◽  
Classification Performance ◽  
Computer Interface ◽  
Eeg Signals ◽  
Spatial Filters ◽  
Improved Performance ◽  
Electroencephalogram Eeg

Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.

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