ABSTRACT
The paper describes a framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the lack of platforms for effective exchange of experimental data, models, and algorithms. The solution to these problems is proposed by construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing efficient methods for neuroscience research.
ABSTRACT
We present an open, parametric system for automatic detection of EEG artifacts in polysomnographic recordings. It relies on independent parameters reflecting the relative presence of each of the eight types of artifacts in a given epoch. An artifact is marked if any of these parameters exceeds a threshold. These thresholds, set for each parameter separately, can be adjusted via "learning by example" procedure (multidimensional minimization with computationally intensive cost function), which can be used to automatically tune the parameters to new types of datasets, environments or requirements. Performance of the system, evaluated on 103 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the inter-expert agreement. To make this statement well defined, we review the methodology of evaluation for this kind of detection systems. Complete source code is available from http://eeg.pl; a user-friendly version with Java interface is available from http://signalml.org.
Subject(s)
Artifacts , Electroencephalography , Polysomnography , Signal Processing, Computer-Assisted , Algorithms , Artificial Intelligence , Blinking , Electrocardiography , Electrodes , Electronics , Eye Movements , Humans , Information Dissemination , Muscles , Sleep/physiology , SoftwareABSTRACT
This paper addresses some practical issues related to the calculation, display and assessment of the significance of changes in the average time-frequency energy density of event-related brain activity. Using scalp EEG and subdural ECoG example datasets, parametric tests are evaluated as a replacement for previously applied computer-intensive resampling methods. The performance of different estimates of energy density, based on matching pursuit, scalogram and spectrogram, and their Box-Cox transformations is evaluated with respect to the assumption of normality required for the t-test, and the consistency of the final results.
Subject(s)
Brain Mapping/methods , Brain/physiology , Adult , Algorithms , Electroencephalography , Evoked Potentials/physiology , Humans , Male , Signal Processing, Computer-AssistedABSTRACT
We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.
