EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder

A Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information.  During  EEG acquisition,   artifacts  are induced due to involuntary eye movements or eye blink, casting adverse effects  on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of  Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy.  One of the designed  SAE models outperforms the  performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study,  may provide a contribution towards the state of  the  art performance on the problem of  EEG based eye state classification

Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm

An automatic artifact extraction system is proposed based on a hybridization of Stone’s BSS and genetic algorithm. This hybridization is called evolutionary Stone’s BSS algorithm (ESBSS). Original Stone’s BSS used short- and long-term half-life parameters as constant values, and the changes in these parameters will be affecting directly the separated signals; also there is no way to determine the best parameters. The genetic algorithm is a suitable technique to overcome this problem by finding randomly the optimum half-life parameters in Stone’s BSS. The proposed system is used to extract automatically the common artifacts such as ocular and heart beat artifacts from EEG mixtures without prejudice to the data; also there is no notch filter used in the proposed system in order not to lose any useful information

Ocular Artifact Detection and Removing from EEG by wavelet families: A Comparative Study

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Biological artifacts like ocular artifact (OA) are one of the main interferences in EEG recordings. Eye blinks and movements of the eyeballs produce a signal known as electrooculogram (EOG) that these are 10 to 100 times stronger than the EEG signal. Due to the frequency range of EEG signal and OA which has overlapping with each other, identify and removing of the EOG artifacts are one of the main challenges for researchers, because an incorrect denoising may lose some of the important information of EEG signals. In this context, our aim is to propose a technique based on wavelet transform for accurate identification of the blink artifact zone and removal of EEG signals. We propose using absolute value of signal reconstructed details for blink zone detection and the efficiency of wavelet families to remove the blink artifact which is evaluated by calculating the mean squared error (MSE) between denoised and clean EEG signals and comparing with the results before and after artifact removing show that db7, sym7, coif5, rbio1.5 and dmey at 4th level are preferable and effective in blink artifact zone detection and db7, coif5, dmey, db5 and db9 respectively provide the best result for blink artifact removing with minimum loss important information. Keywords: EEG, EOG, OA, Wavelet transform, MS

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies

Improving the performance of translation wavelet transform using BMICA

Research has shown Wavelet Transform to be one of the best methods for denoising biosignals. Translation-Invariant form of this method has been found to be the best performance. In this paper however we utilize this method and merger with our newly created Independent Component Analysis method – BMICA. Different EEG signals are used to verify the method within the MATLAB environment. Results are then compared with those of the actual Translation-Invariant algorithm and evaluated using the performance measures Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Signal to Interference Ratio (SIR). Experiments revealed that the BMICA Translation-Invariant Wavelet Transform out performed in all four measures. This indicates that it performed superior to the basic Translation- Invariant Wavelet Transform algorithm producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

脳波信号解析に注目したノイズ除去、特徴抽出、実験観測応用を最適化する数理基盤に関する研究

Electroencephalography (EEG) data inevitably contains a large amount of noise particularly from ocular potentials in tasks with eye-movements and eye-blink, known as electrooculography (EOG) artifact, which has been a crucial issue in the braincomputer- interface (BCI) study. The eye-movements and eye-blinks have different time-frequency properties mixing together in EEGs of interest. This time-frequency characteristic has been substantially dealt with past proposed denoising algorithms relying on the consistent assumption based on the single noise component model. However, the traditional model is not simply applicable for biomedical signals consist of multiple signal components, such as weak EEG signals easily recognized as a noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we newly designed the EEG-EOG signal contamination model for quantitative validations of the artifact removal from EEGs, and then proposed the two-stage wavelet shrinkage method with the undecimated wavelet decomposition (UDWT), which is suitable for the signal structure. The features of EEG-EOG signal has been extracted with existing decomposition methods known as Principal Component Analysis (PCA), Independent Component Analysis (ICA) based on a consistent assumption of the orthogonality of signal vectors or statistical independence of signal components. In the viewpoint of the signal morphology such as spiking, waves and signal pattern transitions, A systematic decomposition method is proposed to identify the type of signal components or morphology on the basis of sparsity in time-frequency domain. Morphological Component Analysis (MCA) is extended the traditional concept of signal decomposition including Fourier and wavelet transforms and provided a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases being independent of each other and uniqueness representation, called the concept of “dictionary”. MCA is applied to decompose the real EEG signal and clarified the best combination of dictionaries for the purpose. In this proposed semi-realistic biological signal analysis, target EEG data was prepared as mixture signals of artificial eye movements and blinks and iEEG recorded from electrodes embedded into the brain intracranially and then those signals were successfully decomposed into original types by a linear expansion of waveforms such as redundant transforms: UDWT, DCT,LDCT, DST and DIRAC. The result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST and DIRAC to represent the baseline envelop, multi frequency wave forms and spiking activities individually as representative types of EEG morphologies. MCA proposed method is used in negative-going Bereitschaftspotential (BP). It is associated with the preparation and execution of voluntary movement. Thus far, the BP for simple movements involving either the upper or lower body segment has been studied. However, the BP has not yet been recorded during sit-to-stand movements, which use the upper and lower body segments. Electroencephalograms were recorded during movement. To detect the movement of the upper body segment, a gyro sensor was placed on the back, and to detect the movement of the lower body segment, an electromyogram (EMG) electrode was placed on the surface of the hamstrings and quadriceps. Our study revealed that a negative-going BP was evoked around -3 to -2 seconds before the onset of the upper body movement in the sit-to-stand movement in response to the start cue. The BP had a negative peak before the onset of the movement. The potential was followed by premotor positivity, a motor-related potential, and a reafferent potential. The BP for the sit-to-stand movement had a steeper negative slope (-0.8 to -0.001 seconds) just before the onset of the upper body movement. The slope correlated with the gyro peak and the max amplitude of hamstrings EMG. A BP negative peak value was correlated with the max amplitude of the hamstring EMG. These results suggested that the observed BP is involved in the preparation/execution for a sit-to-stand movement using the upper and lower body. In summary, this thesis is help to pave the practical approach of real time analysis of desired EEG signal of interest toward the implementation of rehabilitation device which may be used for motor disabled people. We also pointed out the EEG-EOG contamination model that helps in removal of the artifacts and explicit dictionaries are representing the EEG morphologies.九州工業大学博士学位論文 学位記番号:生工博甲第290号 学位授与年月日:平成29年3月24日1 Introduction|2 Research Background and Preliminaries|3 Introduction of Morphological Component Analysis|4 Two-Stage Undecimated Wavelet Shrinkage Method|5 Morphologically Decomposition of EEG Signals|6 Bereitschaftspotential for Rise to Stand-Up Behavior九州工業大学平成28年

Kurtosis-Based Blind Source Extraction of Complex Non-Circular Signals with Application in EEG Artifact Removal in Real-Time

A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources