SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

Abstract: A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective channels to emphases the EEG motor signals. Then the resultant from cross correlation are statistical calculated to extract feature for classifying a left and right finger movements using support vector machine (SVM). The genetic algorithm was applied to find the discriminative frequencies of DFT for the two EEG classes signal. The performance of the proposed method was determined by finger movement classification of 13 subjects and the experiments show that the average accuracy is above 93 percent.

Review of Consumer Wearables in Emotion, Stress, Meditation, Sleep, and Activity Detection and Analysis.

Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals. In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity, especially in field studies. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung, Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation.

Consumer Wearables and Affective Computing for Wellbeing Support

Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

Risk analysis of man overboard scenario in a small fishing vessel

Abstract: One major accident scenario aboard fishing vessels is “man overboard” (MOB). Prevention of this accident scenario would reduce the high fatality rate in the fishing industry. Critical understanding of the risk factors is vital for a robust risk assessment of this accident scenario and to develop interventions. This paper presents the Objected-Oriented Bayesian Network (OOBN) application for risk assessment of the MOB scenario. The OOBN model is developed to probabilistically capture the key accident influencing factors in fragmented structures. The proposed methodology is demonstrated in an accident scenario, and the model captures the dynamic dependencies and interdependencies among basic variables and establishes their degree of influence on the accident occurrence probability. The vulnerability path was identified, and a pre-and post-accident intervention plan was proposed to minimize the accident occurrence and its associated risk. Applying the methodology provides vital safety-based information that could be adopted for small vessel operation and maritime administration regulation.

Consumer Wearables and Affective Computing for Wellbeing Support

Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

Analysis of EEG records in an epileptic patient using wavelet transform.

Abstract: About 1% of the people in the world suffer from epilepsy and 30% of epileptics are not helped by medication. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records. Wavelet transform is used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. The capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals. Wavelet analyses of EEGs obtained from a population of patients can potentially suggest the physiological processes undergoing in the brain in epilepsy onset. A better understanding of the dynamics of the human brain through EEG analysis can be obtained through further analysis of such EEG records.

EEG signal classification using PCA, ICA, LDA and support vector machines

Abstract: In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.

Exercise and the immune system: a model of the stress response?

Abstract: Exercise influences natural immunity, T- and B-cell functions, and cytokine responses, through circulatory (hemodynamic) changes and by endocrine hormones secreted in response to physical stress. The magnitude of the effects on the immune system reflects the intensity, duration and chronicity of the exercise. In this review, Laurie Hoffman-Goetz and Bente Klarlund Pedersen suggest that exercise-immune interactions can be viewed as a subset of stress immunology.

Classification of EEG signals using the wavelet transform

Abstract: The electroencephalogram (EEG) is widely used clinically to investigate brain disorders. However, abnormalities in the EEG in serious psychiatric disorders are at times too subtle to be detected using conventional techniques. This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. The data reduction and preprocessing operations of signals are performed using the wavelet transform. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66% of the normal class and 71% of the schizophrenia class of EEGs, respectively. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification.

Analysis of Electroencephalography (EEG) Signals and Its Categorization–A Study

Abstract: Human brain consists of millions of neurons which are playing an important role for controlling behavior of human body with respect to internal/external motor/sensory stimuli. These neurons will act as information carriers between human body and brain. Understanding cognitive behaviour of brain can be done by analyzing either signals or images from the brain. Human behaviour can be visualized in terms of motor and sensory states such as, eye movement, lip movement, remembrance, attention, hand clenching etc. These states are related with specific signal frequency which helps to understand functional behavior of complex brain structure. Electroencephalography (EEG) is an efficient modality which helps to acquire brain signals corresponds to various states from the scalp surface area. These signals are generally categorized as delta, theta, alpha, beta and gamma based on signal frequencies ranges from 0.1 Hz to more than 100 Hz. This paper primarily focuses on EEG signals and its characterization with respect to various states of human body. It also deals with experimental setup used in EEG analysis.