Tagged: Biomedical measurements

Spatio-temporal linear discrimination for inferring task difficulty from EEG

We present a spatio-temporal linear discrimination method for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required i.e. the algorithm requires no knowledge about the timing and/or spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment

Perceptual salience as novelty detection in cortical pinwheel space

We describe a filter-based model of orientation processing in primary visual cortex (V1) and demonstrate that novelty in cortical “pinwheel” space can be used as a measure of perceptual salience. In the model, novelty is computed as the negative log likelihood of a pinwheel’s activity relative to the population response. The population response is modeled using a mixture of Gaussians, enabling the representation of complex, multi-modal distributions. Hidden variables that are inferred in the mixture model can be viewed as grouping or “binding” pinwheels which have similar responses within the distribution space. Results are shown for several stimuli that illustrate well-known contextual effects related to perceptual salience, as well as results for a natural image.