The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

Brain Computer Interfaces allow the interaction between a person and their environment using signals extracted directly from the brain. One of the most common non-invasive methods of brain signal acquisition is the electroencephalography (EEG). An EEG based BCI system generally involves four steps: preprocessing, feature extraction, feature selection, and classification. In order to design a real applicable BCI system, it is important to provide: good classification performance, adequate computational cost, robustness to variations of the signal between trials and between subjects, and a classifiers model that cope with highly dimensional data. Ensemble of classifiers is a learning model that, with a proper design, can satisfy those conditions, which make them a good match for a BCI application. In this paper, some ensemble of classifiers designs are evaluated and compared with other BCI approaches on three different subjects. In the proposed model, Genetic Algorithm is employed as feature selection method and Wavelet Packet Decomposition as preprocessing procedure. Four fusion methods were applied in the ensembles design, including: Majority Voting, Weighted Majority Voting, Genetic Algorithm for classifier selection and Genetic Algorithm to compute the weights for the Weighted Majority fusion method. The best results were obtained with the Weighted Majority Voting fusion method based on Genetic Algorithm.

In Brain-Computer Interfaces, one of the most relevant tasks is the selection of a subset of features that efficiently describes the EEG signal, excluding redundant and irrelevant features. This procedure reduces the dimensionality of the dataset (avoiding the dimensionality curse) and improves the classification accuracy of the system. One of the most successful models applied for this task is the use of an Evolutionary Algorithm in a wrapper approach. These models produce excellent results but present the drawback of a considerable high processing time, a critical limitation for its application on real Brain-Computer Interfaces (BCI) systems. Quantum-inspired Evolutionary Algorithms can be an alternative wrapper approach for the feature selection task, given that they outperform classical Evolutionary Algorithms in the exploration and exploitation of the search space, obtaining the global solution much faster. These algorithm employs concepts and principles from the Quantum Mechanics to probabilistically describe a set of different states between the classical logic states 0 and 1. In this paper, a Quantum-inspired Evolutionary Algorithm is developed and tested over three different subjects from publicly available datasets. In the proposed model, Wavelet Packet Decomposition is employed to analyze the time-frequency characteristics of the signals, and a Multilayer Perceptron Neural Network is employed as a classifier.

Brain Computer Interfaces allow the interaction between a person and their environment using signals extracted directly from the brain. One of the most common non-invasive methods of brain signal acquisition is the electroencephalography (EEG). An EEG based BCI system requires the processing and translation of the EEG signal into significant features that could be converted into commands for the external agent. This system generally involves four steps: preprocessing, feature extraction, feature selection and classification. To guarantee a good classification performance, the application of a convenient feature selection algorithm has a great importance. However, in the literature the information is scarce about a comparison of the different methods, making very difficult the selection of an useful method for a BCI system. In this paper, six feature selection (all separately applied to EEG data) methods: CFS, Consistency, ReliefF, mRmR, C4.5 and Genetic Algorithm are evaluated and compared for Motor Imagery task classification. The validation is made by the performance of five classifiers commonly employed on EEG data: PNN, RBF, SVM, LDA and k-NN. The best result (93,71% of correct classifications) was obtained by the Genetic Algorithm combined with the LDA classifier.