Abstract
BackgroundElectroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods. New methodWe present SEREEGA, Simulating Event-Related EEG Activity. SEREEGA is a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions. The toolbox is available at https://github.com/lrkrol/SEREEGA. ResultsThe simulated data allows established analysis pipelines and classification methods to be applied and is capable of producing realistic results. Comparison with existing methodsMost simulated EEG is coded from scratch. The few open-source methods in existence focus on specific applications or signal types, such as connectivity. SEREEGA unifies the majority of past simulation methods reported in the literature into one toolbox. ConclusionSEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.
Highlights
SEREEGA is available as an EEGLAB plug-in including a graphical user interface (GUI) that allows the core steps of designing and running a simulation to be performed; see figure 1
In this paper we presented the core functionality of SEREEGA, a free and open-source MATLABbased toolbox to simulate event-related EEG activity
SEREEGA allows any number of components to be defined in a virtual brain model, each with a specific location in the brain, a freely oriented scalp projection, and any number of signals
Summary
Having seen almost a century of continuous research and development since its first application on humans in the 1920s (Berger, 1929), electroencephalography (EEG) is widely used in, among others, clinical settings, neuroscience, cognitive science, psychophysiology, and brain-computer interfacing, while its use continues to expand in fields such as neuroergonomics (Parasuraman & Rizzo, 2007; Frey, Daniel, Castet, Hachet, & Lotte, 2016), neurogaming (Krol, Freytag, & Zander, 2017), neuromarketing (Vecchiato et al, 2011), neuroadaptive technology (Zander, Krol, Birbaumer, & Gramann, 2016) and mobile brain/body imaging (Gramann et al, 2011). Five types of activation signals are provided, allowing the simulation of different types of systematic (eventrelated) activity in both the time and the frequency domain, as well as the inclusion of any already existing time series as an activation signal This toolbox is intended to be a tool to generate data with a known ground truth in order to evaluate neuroscientific and signal processing methods, such as blind source separation, source localisation, connectivity measures, brain-computer interface classifier accuracy, derivative EEG measures, et cetera. We provide an analysis using established neuroscientific and brain-computer interfacing methods of a sample data set created with the toolbox
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