Neurofeedback by means of non-invasive surface Electroencephalography (EEC) or Magnetoencephalography (MEG) of the human brain provides an important mechanism for control of brain- computer-interfaces (BCI) or potential treatment for disorders such as epilepsy and attention deficit hyperactivity disorder (ADHD). It also allows studying functional aspects of the human brain, which are impossible to observe in off-line studies, i.e. those neuronal circuits, which only become activated during feedback. Traditionally, the feedback channels have been the EEC potentials recorded on the scalp surface or their spectral components. Due to volume conductor effects these recordings are blurred representations of the underlying cortical activity. This makes it difficult to assess feedback for specific cortical regions from the channel information alone. Also, the low signal to noise ratio (SNR) of the raw EEC is a limiting factor in BCI or feedback applications. An inverse model, which incorporates the individual subject geometry allows direct imaging of cortical regions and can provide specific functional feedback for these regions. Using an inverse method in combination with application of connectivity measures, such as coherence, higher order spectral analysis (HOSA), phase-locking or a multivariate autoregressive (MVAR) model will enhance neurofeedback applications in two ways: The probability of random interaction between regions due to noise will be lower than single channel measures and the use of a priori spatial information by an inverse method will utilize all avalable channels, thus increasing the SNR and specificity of the system. Non linear methods, such as HOSA, have the advantage of being robust against Gaussian white noise and linear crosstalk effects, which can confound connectivity measures. A real time system based on cortical synchrony measures can be used to train subjects to actively increase or decrease synchronization between selected cortical regions and can facilitate neurofeedback treatment of ADHD or epilepsy. Also, cortical synchrony detection can be directly applied to BCI. We will develop an EEG/MEG cortical imaging system for neurofeedback applications, which will image cortical synchrony between selected regions in real time. We will verify the system by feedback experiments on healthy subjects with the goal to enable subjects to increase or decrease cortical synchrony in selected regions using the proposed imaging system.
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