EEG-based endogenous online co-adaptive brain-computer interfaces: strategy for success?

A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control.C. Vidaurre was supported by grant number RyC-2014-15671 of the Spanish MINECO

Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios

An extended virtual reality framework for controlling an avatar via an SSVEP brain-computer interface

Zsfassung in dt. SpracheSteady-state visual evoked potentials (SSVEPs) are brain signals used to operate Brain-Computer Interface (BCI) systems. SSVEP BCIs offer excellent information transfer rates (ITR). Reliably generating SSVEP stimuli in a virtual reality environment, allows for a much easier implementation of motivating, more realistic simulations of real-world applications. The aims of this thesis are (i) to integrate a highly configurable and flexible virtual reality application, that can generate SSVEP stimuli and display feedback in form of the movements of an avatar. Based on the Studierstube mixed reality framework, three Open Inventor (Coin3D) compatible components (a) StbAvatar (extended), (b) StbBCICommApp (extended) and (c) StbBCIToolbox (from scratch) were integrated and combined in order to create scenarios for both, accuracy measurements with a phototransistor circuit and online EEG experiments with five subjects. The experiments were successful, and showed, that both, moving and static, triangular or rectangular non-VSync software SSVEP stimuli at frequencies between 5 and 29Hz on a standard 60Hz TFT monitor, proved suitable to elicit steady-state visual evoked potentials. The results were reasonably good in direct comparison, with two out of five subjects performing worse by less than 7%. Only one out of five subjects performed significantly worse by 25%. The amplitudes of the peaks at the target frequencies and harmonics in the FFT spectra of the software SSVEP measurements were smaller for all except of one subject, which suggests, that the quality of the software SSVEP stimulation is to a certain degree inferior to that of the LEDs. This research direction could lead to vastly improved immersive virtual environments, that allow both disabled and healthy users to seamlessly navigate, communicate or interact through an intuitive, natural and friendly interface.6

Chapter Brain-Computer Interface Systems Used for Virtual Reality Control

Biotechnolog

Chapter Brain-Computer Interface Systems Used for Virtual Reality Control

Biotechnolog

Evaluation of different EEG acquisition systems concerning their suitability for building a brain-computer interface

One important aspect in non-invasive brain-computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective this means with maximum comfort and without any extra inconveniences (e.g., washing the hair). Whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently.In this work we evaluated three different commercially available EEG acquisition systems that differ in the type of electrode (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort).We found that the water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode system caused the least inconveniences.Therefore, building a reliable BCI is possible with all evaluated systems and it is on the user to decide which system meets the given requirements best