A5047410616- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Advanced MRI Techniques and Applications
- Neural Networks and Applications
- Epilepsy research and treatment
- Neuroscience and Music Perception
- Neuroscience and Neuropharmacology Research
- Visual perception and processing mechanisms
- Sleep and Wakefulness Research
- Neurological disorders and treatments
- Neuroscience and Neural Engineering
- Machine Learning in Bioinformatics
- Tactile and Sensory Interactions
- Attention Deficit Hyperactivity Disorder
- Topological and Geometric Data Analysis
- Atomic and Subatomic Physics Research
- Memory and Neural Mechanisms
- ECG Monitoring and Analysis
- Migraine and Headache Studies
- Neural and Behavioral Psychology Studies
- Computational Drug Discovery Methods
- Mechanical and Optical Resonators
- Neonatal and fetal brain pathology
Heidelberg University
1998-2004
University Hospital Heidelberg
2002-2004
Software (Spain)
2002
Summary Digital EEG allows one to combine recorded channels into new montages without the need record data. Using spherical splines, voltages can be estimated at any point on head. This generate various with or virtual electrodes standardized locations, interpolate bad electrodes, and topographic maps over whole Simulations of activity originating in brain regions are used illustrate effects known generators whole-head maps. Some properties spatial filters introduced, it is shown how they...
Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by presence artifacts. The aim this study is to describe evaluate a fast automatic algorithm ongoing correction artifacts in continuous EEG recordings, which applied offline online. based on blind source separation. It uses sliding window technique with overlapping epochs features spatial, temporal frequency domain detect correct ocular, cardiac, muscle...
The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands events need to be inspected and mentally classified the reviewer. This study investigated a novel technique clustering 24-h hyper-clustering on top assess whether fast review focal interictal types was feasible comparable spikes observed during routine EEG in epilepsy monitoring.Spike used transformation into 29 regional source activities adaptive thresholds...
The comparative sensitivity of EEG and magnetoencephalography (MEG) in the visual detection focal epileptiform activity simultaneous interictal sleep recordings were investigated. authors examined 14 patients aged 3.5 to 17 years with localization-related epilepsy. Simultaneous 122-channel whole-head MEG 33-channel recorded for 20 40 minutes during spontaneous sleep. data separated four blinded independent reviewers marked presence timing epileptic discharges (ED) 28 segments. matched spikes...
Recognizing specific events in medical data requires trained personnel. To aid the classification, machine learning algorithms can be applied. In this context, records are usually high-dimensional, although a lower dimension also reflect dynamics of signal. study, electroencephalogram with Interictal Epileptic Discharges (IEDs) investigated. First, dimensions reduced using Dynamical Component Analysis (DyCA) and Principal (PCA), respectively. The examined topological analysis (TDA),...
A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction the recorded signal, using dynamical component analysis (DyCA) or principal (PCA), a persistent homology resulting phase space trajectories performed. Features are extracted and used to train evaluate support vector machine (SVM). Classification results on these features compared statistical...
Abstract Objective. The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved is presented that much faster. Approach. Accelerated convergence achieved by replacing the natural learning rule of a fully-multiplicative orthogonal-group based update scheme ICA unmixing matrix, leading to orthogonal (OgExtInf). computational performance OgExtInf...
In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with aim facilitating review MEG data containing epileptiform discharges. Test were obtained by superposing simulated signals 100-nAm dipolar sources resting state recording healthy subject. Simulated placed systematically different cortical locations for defining optimal regularization reconstruction and assessing detectability...
The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved is presented that much faster. Accelerated convergence achieved by replacing the natural learning rule of a fully-multiplicative orthogonal-group based update scheme unmixing matrix leading to orthogonal (OgExtInf). Computational performance OgExtInf compared with two fast ICA...
Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by presence artifacts. The aim this study is to describe evaluate a fast automatic algorithm ongoing correction artifacts in continuous EEG recordings, which applied offline online. Methods: based on blind source separation. It uses sliding window technique with overlapping epochs features spatial, temporal frequency domain detect correct...
Introduction: Traditional spike detection programs mark detected events in long-term EEG but lack a good overview. BESA Epilepsy uses new hypercluster technique to combine similar over 24 hours. On daily basis, the physician inspects each and decides whether it is epileptiform. Optimized source waveform segment displays, 3D maps, localization of head scheme allow for fast decision assessment likely region origin.
For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact removal tools. This applies in particular ballistocardiogram (BCG) which difficult remove without distorting signals interest related brain activity. Here, we documented surrogate source models separate artifact-related from with minimal distortion activity interest. The topographies used for separation were created automatically using principal components (PCA-S) or by manual selection utilizing...