This paper focusses on some practical issues that can arise when developing methodologies for calculating benchmark figures for extreme market events, particularly in the context of the Financial Services Authority's ICAS regime. The paper limits discussion to equity and interest rate risks. Whilst not intended to constitute formal guidance, it is hoped that the material contained within the paper will be useful to practitioners. The paper acknowledges the role of prior beliefs in the choice of data to be used for modelling and its influence upon the ensuing results.

P300 amplitude and reaction time (RT) are strongly affected by the sequence of events preceding the eliciting stimulus. Sommer, Leuthold and Soetens (1999) found that robust sequential effects in P300 amplitude could be dissociated from more variable sequential effects in RTs. However, global changes in P300 amplitude and topography gave rise to the suggestion that sequential effects are specific for a subcomponent of P300 that is separate from and anterior to the classical parietal P300. Here, confirming evidence for dissociable subcomponents of P300 is reported from two experiments. Independent component analysis separated a centrally distributed sequence-sensitive subcomponent from a more parietal subcomponent. Subsequent dipole source analysis indicated a deep mesial source for the sequence-sensitive subcomponent. Overlap with reafferent somatosensory activity appears to be responsible for an apparent lateralization of this component towards the hemisphere ipsilateral to the responding hand.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.