Application of cepstrum analysis and linear predictive coding for motor imaginary task classification

In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context

Protein fold recognition using genetic algorithm optimized voting scheme and profile bigram

In biology, identifying the tertiary structure of a protein helps determine its functions. A step towards tertiary structure identification is predicting a protein’s fold. Computational methods have been applied to determine a protein’s fold by assembling information from its structural, physicochemical and/or evolutionary properties. It has been shown that evolutionary information helps improve prediction accuracy. In this study, a scheme is proposed that uses the genetic algorithm (GA) to optimize a weighted voting scheme to improve protein fold recognition. This scheme incorporates k-separated bigram transition probabilities for feature extraction, which are based on the Position Specific Scoring Matrix (PSSM). A set of SVM classifiers are used for initial classification, whereupon their predictions are consolidated using the optimized weighted voting scheme. This scheme has been demonstrated on the Ding and Dubchak (DD), Extended Ding and Dubchak (EDD) and Taguchi and Gromhia (TG) datasets benchmarked data sets

Predict gram - positive and gram - negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou’s general PseAAC

In this study, we used structural and evolutionary based features to represent the sequences of gram-positive and gram-negative subcellular localizations. To do this, we proposed a normalization method to construct a normalize Position Specific Scoring Matrix (PSSM) using the information from original PSSM. To investigate the effectiveness of the proposed method we compute feature vectors from normalize PSSM and by applying Support Vector Machine (SVM) and Naïve Bayes classifier, respectively, we compared achieved results with the previously reported results. We also computed features from original PSSM and normalized PSSM and compared their results. The archived results show enhancement in gram-positive and gram-negative subcellular localizations. Evaluating localization for each feature, our results indicate that employing SVM and concatenating features (amino acid composition feature, Dubchak feature (physicochemical-based features), normalized PSSM based auto-covariance feature and normalized PSSM based bigram feature) have higher accuracy while employing Naïve Bayes classifier with normalized PSSM based auto-covariance feature proves to have high sensitivity for both benchmarks. Our reported results in terms of overall locative accuracy is 84.8% and overall absolute accuracy is 85.16% for gram-positive dataset; and, for gram- negative dataset, overall locative accuracy is 85.4% and overall absolute accuracy is 86.3%

Subject - specific - frequency - band for motor imagery EEG signal recognition based on common spatial spectral pattern

Over the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems

Subspace Support Vector Data Description and Extensions

Machine learning deals with discovering the knowledge that governs the learning process. The science of machine learning helps create techniques that enhance the capabilities of a system through the use of data. Typical machine learning techniques identify or predict different patterns in the data. In classification tasks, a machine learning model is trained using some training data to identify the unknown function that maps the input data to the output labels. The classification task gets challenging if the data from some categories are either unavailable or so diverse that they cannot be modelled statistically. For example, to train a model for anomaly detection, it is usually challenging to collect anomalous data for training, but the normal data is available in abundance. In such cases, it is possible to use One-Class Classification (OCC) techniques where the model is trained by using data only from one class. OCC algorithms are practical in situations where it is vital to identify one of the categories, but the examples from that specific category are scarce. Numerous OCC techniques have been proposed in the literature that model the data in the given feature space; however, such data can be high-dimensional or may not provide discriminative information for classification. In order to avoid the curse of dimensionality, standard dimensionality reduction techniques are commonly used as a preprocessing step in many machine learning algorithms. Principal Component Analysis (PCA) is an example of a widely used algorithm to transform data into a subspace suitable for the task at hand while maintaining the meaningful features of a given dataset. This thesis provides a new paradigm that jointly optimizes a subspace and data description for one-class classification via Support Vector Data Description (SVDD). We initiated the idea of subspace learning for one class classification by proposing a novel Subspace Support Vector Data Description (SSVDD) method, which was further extended to Ellipsoidal Subspace Support Vector Data Description (ESSVDD). ESSVDD generalizes SSVDD for a hypersphere by using ellipsoidal data description and it converges faster than SSVDD. It is important to train a joint model for multimodal data when data is collected from multiple sources. Therefore, we also proposed a multimodal approach, namely Multimodal Subspace Support Vector Data Description (MSSVDD) for transforming the data from multiple modalities to a common shared space for OCC. An important contribution of this thesis is to provide a framework unifying the subspace learning methods for SVDD. The proposed Graph-Embedded Subspace Support Vector Data Description (GESSVDD) framework helps revealing novel insights into the previously proposed methods and allows deriving novel variants that incorporate different optimization goals. The main focus of the thesis is on generic novel methods which can be adapted to different application domains. We experimented with standard datasets from different domains such as robotics, healthcare, and economics and achieved better performance than competing methods in most of the cases. We also proposed a taxa identification framework for rare benthic macroinvertebrates. Benthic macroinvertebrate taxa distribution is typically very imbalanced. The amounts of training images for the rarest classes are too low for properly training deep learning-based methods, while these rarest classes can be central in biodiversity monitoring. We show that the classic one-class classifiers in general, and the proposed methods in particular, can enhance a deep neural network classification performance for imbalanced datasets