Abstract
Gamma band oscillations in the human brain (around 40 Hz) play a functional role in information processing, and a real-time assessment of gamma band activity could be used to evaluate the functional relevance more directly. Therefore, we developed a source based Brain-Computer-Interface (BCI) with an online detection of gamma band activity in a selective brain region in the visual cortex. The BCI incorporates modules for online detection of various artifacts (including microsaccades) and the artifacts were continuously fed back to the volunteer. We examined the efficiency of the source-based BCI for Neurofeedback training of gamma- and alpha-band (8–12 Hz) oscillations and compared the specificity for the spatial and frequency domain. Our results demonstrated that volunteers learned to selectively switch between modulating alpha- or gamma-band oscillations and benefited from online artifact information. The analyses revealed a high level of accuracy with respect to frequency and topography for the gamma-band modulations. Thus, the developed BCI can be used to manipulate the fast oscillatory activity with a high level of specificity. These selective modulations can be used to assess the relevance of fast neural oscillations for information processing in a more direct way, i.e., by the adaptive presentation of stimuli within well-described brain states.
Highlights
Brain-Computer-Interface (BCI) can be used as a non-invasive method to enhance the human ability to regulate electrical brain activity
We designed an advanced BCI method to assist participants to switch between modulation of alpha- and gamma-band oscillations in the visual cortex
The BCI method used an online artifact control for artifact suppression, a special visual display design to avoid distraction and yet motivate volunteers and a source based BCI approach to limit the training to a distinct neural area
Summary
Brain-Computer-Interface (BCI) can be used as a non-invasive method to enhance the human ability to regulate electrical brain activity. BCI applications are applied as active and/or reactive BCI. With electroencephalography (EEG)-based active BCI (Neurofeedback), brain signals are recorded from the scalp and relevant components are extracted in near real-time and fed back to the individual, i.e., in the form of visual information. The individual uses the feedback information in order to learn how to deliberately modify a particular brain activity. Reactive BCIs can be used to trigger specific commands as specific frequencies are classified, such as the movement of a robotic arm or the presentation of visual stimuli
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