Wilhelm Kincses

Modeling extended sources of event-related potentials using anatomical and physiological constraints

For the study of functional organization and reorganization of the human cortex by means of electromagnetic source imaging, a measure of the location and spatial extent of neural sources is of interest. This study evaluates the cortical patch method (CPM), an iterative procedure introduced by Lütkenhöner et al. [1995] that models EEG/MEG activity by means of extended cortical patches. Anatomical information is used to constrain estimates of location and extent of neural sources that generate the measured evoked potential. Whereas minimum norm approaches use mathematical constraints to solve the ambiguity of the inverse problem, the CPM introduces constraints based on anatomical and physiological knowledge about neural mass activity. In order to test the proposed method, the simulated activity in an artificial sulcus was subjected to the CPM. The results show that even activity on opposing walls of a sulcus can be well reconstructed. The simulations demonstrate the usefulness and limits of the CPM in estimating the spatial extent of neural sources in the cerebral cortex. As an example, an application of the method on experimental somatosensory evoked potentials is presented in the Appendix. Hum Brain Mapping 8:182–193, 1999.

Reconstruction of extended cortical sources for EEG and MEG based on a Monte‐Carlo‐Markov‐chain estimator

A new procedure to model extended cortical sources from EEG and MEG recordings based on a probabilistic approach is presented. The method (SPMECS) was implemented within the framework of maximum likelihood estimators. Neuronal activity generating EEG or MEG signals was characterized by the number of sources and their location and extension. Based on the noise distribution of the measured data, source configurations were associated with the according value of the likelihood function. To find the most likely source, i.e., the maximum likelihood estimator, and its level of confidence, a stochastic solver (Metropolis algorithm) was applied. The method presented supports the incorporation of virtually any constraint, e.g., based on physiological and anatomical a priori knowledge. Thus, ambiguity of the ill‐posed inverse problem was reduced considerably by confining sources to the cortical surface extracted from individual MR images. The influence of different levels and types of noise on the outcome was investigated by means of simulations. Somatosensory evoked magnetic fields analyzed by the method presented suggest that larger extended cortical areas are involved in the processing of combined finger stimulation as compared to single finger stimulation. Hum. Brain Mapping 18:100–110, 2003.

EEG alpha spindles as indicators for prolonged brake reaction time during auditory secondary tasks in a real road driving study

Driver distraction accounts for a substantial number of traffic accidents. Therefore, the impact of auditory secondary tasks on driving performance was examined. In addition to performance measures, i.e. reaction time on emergency brakings of a leading vehicle, mental driver states were described by electroencephalographic (EEG: alpha spindles, alpha band power) as well as cardiac activity (ECG: heart rate variability). Results show that brake reaction time (RT) increased with time-on-task during all conditions (p<.001), and was significantly higher while performing the secondary task (p<.001). Physiological measures showed similar effects. Alpha spindle rate, alpha band power as well as heart rate variability (HRV) increased with time-on-task and were significantly different during the secondary task, indicating inhibited visual information processing and reduced concentration ability. This study shows that reduced driving performance measured by means of prolonged brake reactions during increased cognitive load elicited by auditory secondary tasks is indicated by EEG measures as well as cardiac activity, enabling the direct quantification of driver distraction in experiments during real road driving.

Measuring driver’s mental workload using EEG

According to analyses of traffic accident data, 90 % of traffic accidents are caused by driving failures due to an impaired driver mental state [1, 2]. The development of optimal Advanced Driver Assistance Systems (ADAS) requires a profound understanding of the causes and factors that lead to an impaired driver mental state, such as fatigue, inattention or mental overload. However, the interaction of driver, vehicle and environment is extremely complex and the traditional analytical methods of behavioral psychology are often insufficient and difficult to assess. Using EEG, the Daimler AG has brought neurophysiological approaches from the laboratory into the vehicle and is able to perform real-world driving studies.