Brain computer interfacing: A spectrum estimation based neurophysiological signal interpretation

Seth, Debojyoti; Chakraborty, Debashis; Ghosal, Prasun; Sanyal, Sugata

doi:10.1109/spin.2017.8050008

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Other well-known feature extraction and dimension reduction methods such as principal component analysis and independent component analysis are also used frequently to improve the EEG classification accuracy (Ince et al, 2006;Guo et al, 2008;Talukdar et al, 2014). Autoregressive model and power spectral density estimation are also common feature extraction algorithms for EEG classification (Argunsah and Cetin, 2010;Seth et al, 2017). In the classification part, the frequently used classification methods include linear discriminant analysis (Rajaguru and Prabhakar, 2017), Bayesian method (Bashashati et al, 2016), BP neural network (Gao et al, 2012), support vector machine (SVM) (Liu et al, 2012), and so on (Wang et al, 2006;Yang et al, 2012;Roeva and Atanassova, 2016).…”

Section: Introductionmentioning

confidence: 99%

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Huang

Chen

et al. 2020

Front. Neurosci.

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The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals,and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.

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Section: Introductionmentioning

confidence: 99%

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Huang

Chen

et al. 2020

Front. Neurosci.

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“…At present, researchers have used algorithms in various fields to study the feature extraction and classification of EEG signals. The common feature extraction algorithms include: autoregressive model (AR model) (Argunsah and Cetin, 2010), power spectral density estimation (Seth et al, 2017), wavelet transform (Ocak, 2009), chaos method (Lv et al, 2004), common spatial pattern (CSP) (Blankertz et al, 2008;Ramoser et al, 2000), new descriptor (Kapoor et al, 2016), multi-dimensional statistical analysis (Weis et al, 2010) and so on. The frequently used classification methods include Fisher linear discriminant analysis (Harikumar, 2017), Bayesian method (Bashashati et al, 2016), BP neural network (Gao et al, 2012), support vector machine (Hortal et al, 2015) and so on (Roeva and Atanassova, 2016;Yang et al, 2012;Kaper et al, 2004;Wang et al, 2005;Deriche and Alani, 2001).…”

Section: Introductionmentioning

confidence: 99%

EEG Classification Based on Sparse Representation and Deep Learning

Gao¹,

Shang²,

Xiong³

et al. 2018

Neuroquantology

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For brain computer interfaces (BCIs) research, the classification of motor imagery brain signals is a major and challenging step. Based on the traditional sparse representation classification, a classification algorithm of electroencephalogram (EEG) based on sparse representation and convolution neural network is proposed by this paper. For the EEG signal, firstly, the features of the signal are obtained through the common spatial pattern (CSP) algorithm, and then the redundant dictionary with sparse representation is constructed based on these features. The sparse representation of the EEG signal is completed and the sparse features can be obtained. Finally, the sparse features are transformed into two dimensional signals, and the convolution neural network is used to complete the classification of EEG signals. Using the dataset downloaded from the website of BCI competition III (dataset IVa), for two types of EEG signals, the experiments show that the recognition accuracy of the method is over 80, and the recognition accuracy is better than that of the traditional SRC algorithm.

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Statistical Optimization of WARP Radio Board Parameters for Frugal Spectrum Estimation Using AR Model

Chakraborty,

Sanyal

2024

Wireless Pers Commun

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Brain computer interfacing: A spectrum estimation based neurophysiological signal interpretation

Cited by 6 publications

References 7 publications

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

EEG Classification Based on Sparse Representation and Deep Learning

Statistical Optimization of WARP Radio Board Parameters for Frugal Spectrum Estimation Using AR Model

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