Ignas Martisius

Consumer-grade EEG devices: are they usable for control tasks?

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.

A Prototype SSVEP Based Real Time BCI Gaming System

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

EEG Dataset Reduction and Feature Extraction Using Discrete Cosine Transform

Brain-Computer interface (BCI) systems require intensive signal processing in order to form control signals for electronic devices. The majority of BCI systems work by reading and interpreting cortically evoked electro-potentials across the scalp via an electro-encephalogram (EEG). An important factor affecting the efficiency of BCI is the number of EEG features. To reduce the number of features is an important way to improve the speed. In this paper, we consider application of discrete cosine transform (DCT) on EEG signals. DCT takes correlated input data and concentrates its energy in just first few transform coefficients. This method is used as a feature extraction step and allows data size reduction without losing important information. For classification we are using artificial neural networks with different number of hidden neurons and training functions. We conclude that the method can be successfully used for the feature extraction and dataset reduction.

Class-Adaptive denoising for EEG data classification

Brain-computer interface (BCI) systems use electro-encephalogram (EEG) data to control external electronic devices. The main task of BCI systems is to differentiate the classes of mental tasks from the EEG data. The EEG data is inherently complex and difficult to analyze due to interference by eye and muscle movements as well as electrical grid noise. In this paper we analyze shrinkage functions for signal filtering and propose a class-adaptive method for EEG data denoising. The results are evaluated using a Support Vector Machine.

EEG Dataset Reduction and Classification Using Wave Atom Transform

Brain Computer Interface (BCI) systems perform intensive processing of the electroencephalogram (EEG) data in order to form control signals for external electronic devices or virtual objects. The main task of a BCI system is to correctly detect and classify mental states in the EEG data. The efficiency (accuracy and speed) of a BCI system depends upon the feature dimensionality of the EEG signal and the number of mental states required for control. Feature reduction can help improve system learning speed and, in some cases, classification accuracy. Here we consider Wave Atom Transform (WAT) of the EEG data as a feature reduction method. WAT takes input data and concentrates its energy in a few transform coefficients. WAT is used as a data preprocessing step for feature extraction. We use artificial neural networks (ANNs) for classification and perform research with varying number of neurons in a hidden layer and different network training functions (Levenberg-Marquardt, Conjugate Gradient Backpropagation, Bayesian Regularization). The novelty of the paper is the application of WAT in the EEG data processing. We conclude that the method can be successfully used for feature extraction and dataset feature reduction in the BCI domain.

Design of a Neural Interface Based System for Control of Robotic Devices

The paper describes the design of a Neural Interface Based (NIS) system for control of external robotic devices. The system is being implemented using the principles of component-based reuse and combines modules for data acquisition, data processing, training, classification, direct and the NIS-based control as well as evaluation and graphical representation of results. The system uses the OCZ Neural Impulse Actuator to acquire the data for control of Arduino 4WD and Lynxmotion 5LA Robotic Arm devices. The paper describes the implementation of the system’s components as well as presents the results of experiments.