A fast fixed-point algorithm for independent component analysis
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Applications of second order blind identification to high-density EEG-Based brain imaging: a review
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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Top-down and bottom-up processing are two distinct yet highly interactive modes of neuronal activity underlying normal and abnormal human cognition. Here we characterize the dynamic processes that contribute to these two modes of cognitive operation. We used a blind source separation algorithm called second-order blind identification (SOBI [1]) to extract from high-density scalp EEG (128 channels) two components that index neuronal activity in two distinct local networks: one in the occipital lobe and one in the frontal lobe. We then applied Granger causality analysis to the SOBI-recovered neuronal signals from these two local networks to characterize feed-forward and feedback influences between them. With three repeated observations made at least one week apart, we show that feed-forward influence is dominated by alpha while feedback influence is dominated by theta band activity and that this direction-selective dominance pattern is jointly modulated by situational familiarity and demand for visual processing.