Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
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A temporally-constrained blind-source-separation algorithm was used to analyse auditory evoked potentials, evoked from impulse trains with inter-stimulus rates of 95 and 198 Hz. A nonstationarity of variance contrast function was used, and a simulation run showing its ability to extract sources based on a simple convolved model of auditory brainstem and middle latency responses. For a stimulus rate of 95 Hz, where no neural adaptation occurs, this approach was partially successful for experimental data. For the higher rate of 198 Hz particularly poor results were observed for brainstem responses. It is hypothesised that this may be due to the neural adaptation process and/or an inappropriate choice of source model.