Robot Motion Control Using the Emotiv EPOC EEG System

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%

SSVEP-based brain-computer interface for computer control application using SVM classifier

n this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control appli-cations using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activi-ties or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine

USING STUDENT MENTAL STATE AND LEARNING SENSORY MODALITIES TO IMPROVE ADAPTIVITY IN E-LEARNING

  In this paper, we present an innovative solution to improve adaptivity in an e-learning system using Brain Computer Interface (BCI) measures (Attention/Meditation) in order to detect changes in students’ preferred perceptual modes for learning information (VARK model). Our solution is also able to report course units and learning resources that could be difficult for the students

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications

Método automatizado para la evaluación de la usabilidad en sistemas e-learning usando monitoreo de actividad cerebral

Los métodos de evaluación de la usabilidad que miden la satisfacción, se caracterizan por el uso de instrumentos de indagación como cuestionarios y/o entrevistas, los cuales se aplican a usuarios finales luego de la interacción con el software. Esto implica un cierto grado de subjetividad en los resultados obtenidos, ya que dichos instrumentos pueden ser mal interpretados y su diligenciamiento depende de la recordación y no de información tomada en el momento mismo de la interacción. Dado este contexto, se propone la definición de un método automatizado de evaluación de la satisfacción, basado en el monitoreo de la actividad cerebral (BCI), estableciendo así, una métrica y un método innovador que captura datos en el tiempo real de la interacción y genera autónomamente información relativa a la medida de la satisfacción. Para efectos de la validación, se aplicó en objetos de aprendizaje para entornos e-learning. Los resultados arrojados indican que la variable Atención calculada a partir del monitoreo de la actividad cerebral del usuario durante el tiempo de la interacción puede ser usada como métrica confiable para la medida de la satisfacción.Abstract: Evaluation methods of measuring usability satisfaction are characterized by the use of inquiry instruments such as questionnaires and / or interviews that are applied to end users after the interaction process. The above implies a certain degree of subjectivity in the results, as these instruments can be misinterpreted and depends on information not taken at the moment of interaction. Given this context, it is propose an automated method of satisfaction evaluation based on the monitoring of brain activity (BCI), so, It is establishing a metric and an innovative method to capture data in real time interaction and autonomously generates information on the measure of satisfaction. For validation purposes, the method was applied in learning objects for e-learning environments. Results indicate that attention variable calculated from cerebral monitoring user activity during the time of interaction can be used as reliable metric for measuring satisfaction.Maestrí