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An Introduction To The Event Related Potential Technique

This paper describes the OpenViBE software platform which enables researchers to design, test, and use brain--computer interfaces (BCIs). BCIs are communication systems that enable users to send commands to computers solely by means of brain activity. BCIs are gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). The key features of the platform are (1) high modularity, (2) embedded tools for visualization and feedback based on VR and 3D displays, (3) BCI design made available to non-programmers thanks to visual programming, and (4) various tools offered to the different types of users. The platform features are illustrated in this paper with two entertaining VR applications based on a BCI. In the first one, users can move a virtual ball by imagining hand movements, while in the second one, they can control a virtual spaceship using real or imagined foot movements. Online experiments with these applications together with the evaluation of the platform computational performances showed its suitability for the design of VR applications controlled with a BCI. OpenViBE is a free software distributed under an open-source license.

/pdf/openvibe-an-open-source-software-platform-to-design-test-and-2navw1t228.pdf

Openvibe: An open-source software platform to design, test, and use brain--computer interfaces in real and virtual environments

Goal: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. Methods: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. Results: Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA $(0.74 \pm 0.09)$ and LCMV $(0.72 \pm 0.08)$ source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA $(0.74 \pm 0.16)$ but significantly better for LCMV $(0.82 \pm 0.12)$ . Conclusion: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. Significance: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain–computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

/pdf/real-time-neuroimaging-and-cognitive-monitoring-using-2iadc62jps.pdf

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom

Background Researchers in environmental psychology, health studies and urban design are interested in the relationship between the environment, behaviour settings and emotions. In particular, happiness, or the presence of positive emotional mindsets, broadens an individual’s thought-action repertoire with positive benefits to physical and intellectual activities, and to social and psychological resources. This occurs through play, exploration or similar activities. In addition, a body of restorative literature focuses on the potential benefits to emotional recovery from stress offered by green space and ‘soft fascination’. However, access to the cortical correlates of emotional states of a person actively engaged within an environment has not been possible until recently. This study investigates the use of mobile electroencephalography (EEG) as a method to record and analyse the emotional experience of a group of walkers in three types of urban environment including a green space setting. Methods Using Emotiv EPOC, a low-cost mobile EEG recorder, participants took part in a 25 min walk through three different areas of Edinburgh. The areas (of approximately equal length) were labelled zone 1 (urban shopping street), zone 2 (path through green space) and zone 3 (street in a busy commercial district). The equipment provided continuous recordings from five channels, labelled excitement (short-term), frustration, engagement, long-term excitement (or arousal) and meditation. Results A new form of high-dimensional correlated component logistic regression analysis showed evidence of lower frustration, engagement and arousal, and higher meditation when moving into the green space zone; and higher engagement when moving out of it. Conclusions Systematic differences in EEG recordings were found between three urban areas in line with restoration theory. This has implications for promoting urban green space as a mood-enhancing environment for walking or for other forms of physical or reflective activity.

http://static.squarespace.com/static/5128153ce4b000d46396eea9/t/5160526ee4b080e511730972/1365267054886/Aspinall%20et%20al.,%202013__EEG.pdf

The urban brain: analysing outdoor physical activity with mobile EEG

To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77%) and outdoor (69%) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.

How about taking a low-cost, small, and wireless EEG for a walk?

This study presents first evidence that reliable EEG data can be recorded with a new cEEGrid electrode array, which consists of ten electrodes printed on flexible sheet and arranged in a c-shape to fit around the ear Ten participants wore two cEEGrid systems for at least seven hours Using a smartphone for stimulus delivery and signal acquisition, resting EEG and auditory oddball data were collected in the morning and in the afternoon six to seven hours apart Analysis of resting EEG data confirmed well-known spectral differences between eyes open and eyes closed conditions The ERP results confirmed the predicted condition effects with significantly larger P300 amplitudes for target compared to standard tones and a high test-retest reliability of the P300 amplitude (r > = 74) Moreover, a linear classifier trained on data from the morning session revealed similar performance in classification accuracy for the morning and the afternoon sessions (both > 70%) These findings demonstrate the feasibility of concealed and comfortable brain activity acquisition over many hours

/pdf/unobtrusive-ambulatory-eeg-using-a-smartphone-and-flexible-yulsmz4mh2.pdf

Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear

Objective. In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et?al (2012 Psychophysiology 49 1449?53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system. Approach. We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain?computer interface (BCI) application. N = 13 individuals repeatedly performed a 6???6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical. Main results. Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77). Significance. We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.

https://iopscience.iop.org/article/10.1088/1741-2560/11/3/036008/pdf

P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.

Electroencephalography (EEG) allows the study of the brain–behavior relationship in humans. Most of what we have learned with EEG was through observing the brain–behavior relationship under well-controlled laboratory conditions. However, by reducing “normal” behavior to a minimum the ecological validity of the results can be limited. Recent developments toward mobile EEG solutions allow to study the brain–behavior relationship outside the laboratory in more natural situations. Besides mobility and robustness with respect to motion, mobile EEG systems should also interfere as little as possible with the participant's behavior. For example, natural interaction with other people could be hindered when it is obvious that a participant wears an EEG cap. This study evaluates the signal quality obtained with an unobtrusive solution for EEG monitoring through the integration of miniaturized EEG ton-electrodes into both a discreet baseball cap and an individualized ear piece. We show that such mini electrodes located at scalp and ear locations can reliably record event related potentials in a P300 brain–computer–interface application.

https://physoc.onlinelibrary.wiley.com/doi/pdf/10.14814/phy2.12362

Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?

Neurofeedback-guided motor imagery training (NF-MIT) has been suggested as a promising therapy for stroke-induced motor impairment. Whereas much NF-MIT research has aimed at signal processing optimization, the type of sensory feedback given to the participant has received less attention. Often the feedback signal is highly abstract and not inherently coupled to the mental act performed. In this study, we asked whether an embodied feedback signal is more efficient for neurofeedback operation than a non-embodiable feedback signal. Inspired by the rubber hand illusion, demonstrating that an artificial hand can be incorporated into one’s own body scheme, we used an anthropomorphic robotic hand to visually guide the participants’ motor imagery act and to deliver neurofeedback. Using two experimental manipulations, we investigated how a participant’s neurofeedback performance and subjective experience were influenced by the embodiability of the robotic hand, and by the neurofeedback signal’s validity. As pertains to embodiment, we found a promoting effect of robotic-hand embodiment in subjective, behavioral, electrophysiological and electrodermal measures. Regarding neurofeedback signal validity, we found some differences between real and sham neurofeedback in terms of subjective and electrodermal measures, but not in terms of behavioral and electrophysiological measures. This study motivates the further development of embodied feedback signals for NF-MIT.

/pdf/embodied-neurofeedback-with-an-anthropomorphic-robotic-hand-4rw6q9xlcb.pdf

Reiner Emkes

Papers

How about taking a low-cost, small, and wireless EEG for a walk?

Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear

P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.

Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?

Embodied neurofeedback with an anthropomorphic robotic hand