Classification of electroencephalogram (EEG) signals is an important task in the brain computer interface system. This paper presents two combination strategies of feature extraction on EEG signals. In the first strategy, Autoregressive coefficients and approximate entropy are calculated respectively, and the features are obtained by assembling them. In the second strategy, the EEG signals are first decomposed into sub-bands by wavelet packet decomposition. Wavelet packet coefficients are then sent to the autoregressive model to calculate autoregressive coefficients, which are used as features extracted from the original EEG signals. These features are fed to support vector machine for classifying the EEG signals. The classification accuracy has been used for evaluating the classification performance. Experimental results in five mental tasks show that the combination strategies can effectively improve the classification performance when the order of autoregressive model is greater than 5, and the second strategy is superior to the first one in terms of the classification accuracy.

Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition

Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface MI-BCI for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic EEG sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

Electroencephalographic motor imagery brain connectivity analysis for bci: A review

Electroencephalography (EEG) signal has been used widely in health and medical fields. It is also used in brain-computer interface (BCI) systems for humans to continuously control mobile robots and wheelchairs. Recently, the research communities successfully explore the potential of using EEG as a new type of biometrics in user authentication. EEG-based user authentication systems have the combined advantages of both password-based and biometric-based authentication systems, yet without their drawbacks. In this paper, we propose to take the advantage of rich information, such as age and gender, carried by EEG signals for user authentication in multi-level security systems. Our experiments showed very promising results for the proposed multi-factor EEG-based authentication method.

Multi-factor EEG-based user authentication

Multimodal Biometric Authentication using PSO based Watermarking

The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention, which has received increasing attention in rehabilitation. How to extract the features of MI-EEG accurately and quickly is the key to its successful application. Based on the analysis and comparison of the existing feature extraction algorithms, a feature extraction method based on principal component analysis (PCA) and deep belief networks (DBN) is proposed, namely PCA-DBN. Firstly, the second-order moment is used to analyze the time-domain of MI-EEG, select the effective time interval. Secondly, PCA is used to analyze the selected time-domain interval and obtain the principal component feature points. Then, feature points are imported into DBN to realize the final feature extraction. Finally, use the softmax classifier to complete task classification. Perform algorithm validation on the BCI Competition II Data set III and BCI Competition IV Data sets 2b, classification accuracies are 96.25% and 91.71%, kappa values are 0.925 and 0.8342. The paired-sample t-test with FDR correction is carried out on the verification results, and the comparison with some better classification algorithms shows that the algorithm has better performance. In the end, this method is used to extract the features of laboratory data, the optimal classification accuracy is 97.69% and kappa value is 0.9538, the validity of the method is further verified.

/pdf/a-motor-imagery-eeg-feature-extraction-method-based-on-2hrhmc7zgz.pdf

A Motor Imagery EEG Feature Extraction Method Based on Energy Principal Component Analysis and Deep Belief Networks

We propose a new remote multimodal biometric authentication framework based on fragile watermarking for transferring multi-biometrics over networks to server for authentication. A facial image is used as a container to embed other numeric biometrics features. The proposed framework enhances security and reduces bandwidths. In order to reduce error rates from embedding numeric information, we also propose a new method to determine bit priority level in a bit sequence representing the numerical information to be embedded and combine with the current amplitude modulation watermarking method.

/pdf/remote-multimodal-biometric-authentication-using-bit-2cdn617fnt.pdf

Remote multimodal biometric authentication using bit priority-based fragile watermarking

Support vector data description (SVDD) has been widely used in pattern classification, however it does not provide high performance in brain-computer interface (BCI) classification problems since brain signals are noisy and chaotic Brain data have distinct distributions and hence a hyper-sphere in SVDD could not well describe the data We propose in this paper a multi-sphere approach to SVDD to have a better description for the brain data We also propose a fuzzy clustering approach to optimize SVDD parameters Experiments on the brain data set III for motor imagery problem in BCI Competition II were conducted to compare performance of SVDD and multi-sphere SVDD

Multi-sphere support vector data description for brain-computer interface

Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments, however there is no theoretical work on comparison of these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points which can be transformed to hyperplane or hypersphere. Therefore SVM and SVDD are regarded as special cases of this proposed model. We applied the proposed model to analyse the dataset III for motor imagery problem in BCI Competition II and achieved promising results.

Generalised support vector machine for brain-computer interface

In remote biometric authentication systems, raw biometric data stored in centralized databases or being transferred in communication channels are normally encrypted for security purposes. However, encryption does not provide security once the data are decrypted. Biometric watermarking can overcome this problem. The decrypted biometric data are still embedded in an image container and are only retrieved if a secret key is provided. It has been observed that retrieval error is very high if the embedded biometric data are numeric. We proposed a new method based on amplitude modulation and bit priority to embed high priority bits at good positions to reduce the retrieval error when they are converted to numeric data. Experimental results show a significant error reduction for the proposed method.

https://researchsystem.canberra.edu.au/ws/files/29136923/Binder1_bit.pdf

Bit priority-based biometric watermarking

In this paper, we propose a multi-biometrics authentication system based on our proposed priority-based watermarking method. We investigate how watermarking techniques affect the container, which is facial image used in further authentication steps. We conduct experiments on facial and fingerprint features using both priority-based watermarking method and non-priority-based method. Results show that the proposed priority-based watermarking method has reduced data retrieval errors from the facial image after decoding, thus it has also reduced authentication error rates.

/pdf/priority-watermarking-based-face-fingerprint-authentication-2gu9zhx4dw.pdf

Priority Watermarking-Based Face-Fingerprint Authentication System

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