OpenVibe

Abstract: 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.

Abstract: Viable usage of Brain-Computer Interface (BCI) in real-time applications significantly relies on the pre-processing techniques applied on the detected electroencephalography (EEG) signals In EEG, sensorimotor (SMR)/oscillatory signals, such as mu and beta rhythm based BCIs, can be used to restore motor function by neuro-plasticity applied to re-establish normal brain function This study is based on the evaluation of the foot motor execution (ME) and motor imagery (MI), in order to design a BCI neurorehabilitation system Because foot ME and MI reflect the user's physical and imagination state of foot movement respectively, in order to be used as control signals, their appropriate translation is the basic challenge This paper mainly focuses on the quantification and investigation of mu-beta event-related desynchronization (ERD) and event-related synchronization (ERS), for inter and intra-subject variability, making use of the available design tools in open-source platforms such as the OpenViBE software Results show that the frequency of the most reactive components for mu was 88±05 Hz and 213±04 Hz for beta Interestingly a contralateral dominance was visible at electrode position C3 during right foot ME/MI tasks The results have enabled the implementation of a good platform for left-right foot ME/MI discrimination based BCI applications

Abstract: This paper presents a motor imagery based Brain Computer Interface (BCI) that uses single channel EEG signal from the C3 or C4 electrode placed in the motor area of the head. Time frequency analysis using Short Time Fourier Transform (STFT) is used to compute spectrogram from the EEG data. The STFT is scaled to have gray level values on which Grey Co-occurrence Matrix (GLCM) is computed. Texture descriptors such as correlation, energy, contrast, homogeneity and dissimilarity are calculated from the GLCM matrices. The texture descriptors are used to train a logistic regression classifier which is then used to classify the left and right motor imagery signals. The single-channel motor imagery classification system is tested offline with different subjects. The average offline accuracy is 87.6%. An online BCI system is implemented in openViBE with the single channel classification scheme. The stimuli presentations and feedback are implemented in Python and integrated with the openViBe BCI system.

Abstract: In this paper, we present a P300 model for control of Cerebot – a mind-controlled humanoid robot, including a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects’ mental activities regarding robot-walking behavior.