P300 Speller Based ALS Detection Using Daubechies Wavelet Transform in Electroencephalograph

Elimination of EOG signals from raw EEG signals using step size based recursive least squares- least mean fourth adaptive algorithm

In monitoring the depth of anaesthesia, raw EEG signals are recorded by means of an adhesive sensor attached to the forehead. The raw EEG signal is often corrupted by spike, low frequency and high frequency noise. Removal of such noise improves clinical utility and this paper presents a novel method which uses a double wavelet-based de-noising algorithm. The results of experimental simulations show that the proposed method reproduces the EEG signal almost noiselessly. The resultant data is suitable input for monitoring the depth of anaesthesia. We propose to build up a wavelet-based Depth of Anaesthesia (WDoA) based on discrete wavelet transform (DWT) and power spectral density (PSD) function. Findings give very close correlation between the WDoA and BIS Index values, through the whole scale from 100 to 0 with full recording time on patient. Simulation results demonstrate that this new index, WDoA, represents the DoA in all anaesthesia states reliably and accurately.

This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.

This study evaluated the capability of neural classifier to perform the separation between epileptiform and non-epileptiform events. To processing the EEG signals was used the Wavelet Transform through the use of the Coiflet1 function. The main elements present in the EEG signals were separated in five distinct event classes (spikes, sharp waves, blinks, background activity and noise). All the events were processed from first to the tenth decomposition level of the Wavelet Transform, where were generated graphics with the dispersion of each event class. Some experiments were made to try define a decision threshold to separate the groups of elements using the Coiflet1 function. The obtained results showed that only the amplitude of a decomposed signal don't show a distinction between the events classes. Thus, the raw epochs of EEG signals were applied directly in the neural network inputs. To evaluate the neural networks was used the method of cross-validation with early stopping. For the neural classifier was used ROC analysis and performance indexes applied to the diagnostic tests. The experiments have shown that the use of any epoch of training, indicated by the performance indexes (AUC and accuracy) showed the better results. The epochs indicated by the performance indexes were located close to the epoch indicated by the early stopping. The evaluation through of those indexes showed be an efficient method to verify the performance of the classifier, getting the following performance values: AUC index of 0,99910, sensitivity of 97,14%, specificity of 94,55% and an accuracy of 96,14%.

Privacy for Personal Neuroinformatics

Parkinson's disease (PD) is a neurological disorder which is progressive in nature. Although there is no cure to this disease, symptomatic treatments are available. These treatments can slow the progressive development of the symptoms. Medications can treat some of the symptoms of the PD up to a great extent that in turn may help the patients to live a normal life. Besides these medications, the patients can also be provided with various therapies based on the types of their symptoms. But for providing any treatment, detection of its symptoms at an early stage is very important. This can reduce its future complexities considerably. Early diagnosis along with proper medications may treat the symptoms of PD significantly. This motivates to propose a new and effective methodology for detection and analysis of PD. In this work, an approach has been proposed for identification of PD patients by using Electroencephalogram (EEG) signals. Here, the EEG signals of normal persons and PD patients are processed in three stages. First, the raw EEG signals are pre-processed for removal of noises and artefacts present. Out of various techniques, Wavelet transform is used for this purpose. In the MATLAB environment, de-noising can be executed by using the in-built functions. Performances of the de-noising techniques are examined with the performance parameters namely Root Mean Square Error (RMSE) as well as Signal to Noise Ratio (SNR). In the second stage, statistical features are extracted from the pre-processed EEG signals. In this work, five statistical features are considered for performing the classification. In the final stage, the extracted features are classified using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. ANN is an efficient classifier that predicts the human brain's working manners. SVM on the other hand has been proven as one of the most prevailing classification algorithms that gives highly accurate and robust results. This is a novel approach of analyzing the performances of the classification techniques by evaluating the best performing feature. In both the classifiers accuracy, precision, sensitivity and specificity are calculated from the confusion matrix evaluated from the values of the statistical features. In ANN, results using six different training algorithms at different hidden layers are calculated and compared. This proves the training algorithm Levenberg-Marquardt back-propagation with hidden layer 20 as the best combination for performing the classification. From the results it is seen that both ANN and SVM classifiers provide significant classification accuracies of 94.7% and 96.5% respectively. Amongst the five considered features, Mean performs the best in terms of classification accuracy.

In this study cyclic changes of human sleep structure were examined. For whole-night polysomnograms of 35 healthy volunteers of both sexes, manual hypnograms were created and divided into NREM-REM cycles. EEG signals from C3-A2 derivation were analysed by computer using a Fast Fourier Transform (FFT). For consecutive NREM-REM cycles of individual sleep stages, EEG power density contents for delta, theta, alpha, sigma and beta waves were analysed. For consecutive sleep cycles, a clear decrease in NREM sleep duration, especially slow wave sleep duration, was obtained. In addition, a decrease in power density of delta waves was observed. For consecutive sleep cycles, increases in REM sleep duration and in power density of theta and alpha waves were obtained. In consecutive sleep cycles, high amplitude delta slow waves are replaced by higher frequency and lower amplitude waves. Thus stages of NREM sleep are replaced by stages of REM.

Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI. In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size. However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms. In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates. Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.

Comparative exploration on EEG signal filtering using window control methods

Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method

Imagined speech is a process in which a person imagines words without saying them. Electroencephalogram (EEG)-based brain–computer interface (BCI) systems help in automatically identifying imagined speech to facilitate persons with severe brain disorders. Extracting meaningful information from the raw EEG signal is a challenging task due to the nonstationary nature of EEG signals. Decomposing a signal into several sub-bands (SBs) using rational dilation wavelet transform (RADWT) requires selecting predefined factual parameters, which is an arduous task. The main objective of this study is to propose an adaptive RADWT method capable of decomposing EEG signals by adaptively selecting the tuning parameters and classifying the EEG signals into distinct categories. The optimum tuning parameters of RADWT are obtained using particle swarm optimization (PSO) and used to decompose the EEG signals into several SBs. Several statistical features are elicited from each SB and used to input six different machine learning (ML) algorithms. This work uses a 64-channel EEG dataset recorded from 15 healthy people for three categories: long words, short words, and vowels. The performance of the proposed AISR system is evaluated using seven performance evaluation metrics: accuracy, recall, precision, Cohen’s kappa, F1-score, and area under the curve. The proposed system achieved the average classification accuracies of 87.26% ± 1.12%, 89.23% ± 0.95%, 95.5% ± 0.68%, and 92.16% ± 0.83% for long words, short–long words, short words, and vowels, respectively. When compared with the existing state-of-the-art, the proposed non-parametric decomposition approach and the Bagging algorithm achieved a 3%–5% improvement. The performance of the proposed method is validated using an open-access dataset.

Human brain known commonly as an individual's computer is one of the complex organs in the body. The brain serves to control and coordinate the mental and physical actions. It has the capacity to store innumerable data. This data includes not only childhood memories but also those related to the present age. Tracking the electrical potentials or impulses, commonly known as brain waves, can be utilized for various purposes ranging from diagnosing diseases to decoding personality traits. This can be done using the technique of Electroencephalography (EEG). The amount of data available is exploding so also the opportunities of data mining thereby mounting privacy risks. The current practice either prevents the reuse of raw EEG signals or disrespects participants' right to privacy by using this data for purposes other than those consented to, by the individual. The aim of this work is to put a stop to such privacy risks by extracting and transferring only those features related to an application. Feature is a specific characteristic measurement of a pattern segment. EEG signal processing pertains mainly to feature extraction. There are different methods for extracting features from EEG signals. In this work, the features related to neurological diseases, namely epilepsy and Alzheimer's disease, are extracted using Discrete Wavelet Transform (DWT) and those related to Schizophrenia are extracted using Autoregressive (AR) method. For securing the raw data, it is doubly encrypted using Advanced Encryption Standard (AES) algorithm and is kept under the control of each user. The proposed system thus ensures the privacy of an individual's EEG signals, and mitigates any risk that may arise if raw data is made available as such to the public.

Introductions: Medical students are required to lifelong learning. Electroencephalog-raphy is a neuroscience tool that can be used to record brain wave activity related to a person's cognitive function. Objective: This study was conducted to see the relationship between self-regulated learning and cognitive function with quantitative electroencepha-logram (qEEG) parameters. Methods: This study used an observa-tional analytic approach to assess self-regulated learning and cog-nitive function using the MSLQ and MoCA-INA questionnaires. qEEG recording to see brain wave activity was done when the eyes were open and closed and seen in alpha and beta waves. The sam-ple of this study amounted to 32 respondents who were willing to carry out each research procedure. Results: The study findings reveal a significant correlation between SRL and qEEG parameters. In the open-eye condition, such a correla-tion exists in the frontal and central regions with regard to alpha waves (P = 0.046 and P = 0.047). In contrast, in the closed-eye condition, it exists in the occipital, central, and parietal regions with alpha waves (P = 0.005, P = 0.021, and P = 0.049) and the central region with beta waves (P = 0.030). There is a significant correla-tion between cognitive function and qEEG parameters observed in both open and closed-eye conditions within the frontal region beta waves (P = 0.024, P = 0.044, and P = 0.021). Conclusions: There is a relationship between SRL and cognitive function with alpha and beta wave activity on qEEG recording.

Ocular Artifacts (OAs) have a significant impact on the performance of Electroencephalogram (EEG) activities in the frontal region because of its higher amplitude. In this paper, Robust Variable Forgetting Factor (RVFF) and Recursive Least Square (RLS) based RVFF-RLS algorithm is implemented for removal of OAs from the raw EEG signal. Reference signals such as horizontal electro-oculogram and vertical electro-oculogram are recorded and then processed through the finite impulse response filter, whose coefficients are adaptively updated using the RVFF-RLS algorithm. Thereafter, obtained signals are subsequently subtracted from the raw EEG signal to obtain an EEG signal, which is free from OAs. The performance of proposed technique is compared with conventional techniques such as numerical variable forgetting factor RLS, fixed step size normalized least mean squares, fixed forgetting factor- RLS. The proposed technique shows least mean square error under a dynamic environment.

ABSTRACT
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\nPutri, Anita Kurnia. 2020. “Analisis Kontrastif Adverbia Tinkatan Penanda Negasi dalam Bahasa Jepang dan Bahasa Jepang”. Thesis, department of Japanese Language and Culture, Faculty of Humanities, Diponegoro University. The Advisor: Lina Rosliana, S.S., M.Hum.
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\nThe problems that will be discussed in this thesis include: 1. What is the structure and meaning of the adverb in the level of negation markers in Japanese sentences? 2. What is the structure and meaning of the adverb at the level of the negation marker in Indonesian sentences? 3. How are the adverbs of the level of negation markers similar and different in Japanese and Indonesian sentences?
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\nMeanwhile, the purpose of this study is to describe the structure and meaning of adverbs in Japanese and Indonesian sentences. After knowing the structure and meaning, we will examine the similarities and differences in the structure and meaning of the adverbial levels of the negation markers of the two languages.
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\nIn analyzing the data, this study used the agih method followed by contrastive techniques. The data were obtained from the Japanese language website and news articles in Indonesian. Then the data is presented in words or descriptive.
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\nBased on the research, the structural similarities are both followed by verbs, adjectives. While the difference, adverbs in Japanese can be followed by phrases or clauses and negative forms, Indonesian adverbial followed by nouns. In meaning, the adverbs of the level of negation markers in Japanese and Indonesian indicates the meaning of the situation, the action is under adequate. And in terms of meaning it has 7 differences.
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\nKeywords : contrastive, adverbs, teido no fukushi
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