Affective startle modulation in the electromyographic (EMG), auditory startle evoked potentials, and visually evoked potentials (VEPs) were assessed while subjects evaluated pleasant, unpleasant, and neutral adjectives. Acoustic startle probes were presented at random time points 2.5-4.0 s after word onset. The visual P2 and P3 potentials were generally larger during processing of emotional than of neutral adjectives. In contrast, the late positive component was enhanced and was correlated with larger EMG startle responses and auditory startle evoked potential P3 amplitudes for pleasant words only. During internal cognitive activity, the startle reflex represents a measure of "processing interrupt." Thus the startle tone interrupted processing of particularly pleasant adjectives and caused re-alerting to environmental stimuli. Specific effects for pleasant material may arise from a "positivity offset," favoring responses to pleasant material at lower arousal levels.

Electroencephalographic event-related brain potentials were recorded as subjects read, without further instruction, consecutively presented sequences of words. We varied the speed at which the sequences were presented (3 Hz and 1 Hz) and the words' emotional significance. Early event-related cortical responses during reading differentiated pleasant and unpleasant words from neutral words. Emotional words were associated with enhanced brain responses arising in predominantly left occipito-temporal areas 200 to 300 ms after presentation. Emotional words were also spontaneously better remembered than neutral words. The early cortical amplification was stable across 10 repetitions, providing evidence for robust enhancement of early visual processing of stimuli with learned emotional significance and underscoring the salience of emotional connotations during reading. During early processing stages, emotion-related enhancement of cortical activity along the dominant processing pathway is due to arousal, rather than valence of the stimuli. This enhancement may be driven by cortico-amygdaloid connections.

EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section.

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Psychophysiological science employs a large variety of signals from the human body that index the activity of the peripheral nervous system. This allows for studying interactions of psychological and physiological processes that are relevant for understanding cognition, emotion, and psychopathology. The multidimensional nature of the data and the interactions between different physiological signals represent a methodological and computational challenge. Analysis software in this domain is often limited in its coverage of the signals from different physiological systems, and therefore only partially addresses these challenges. ANSLAB (short for Autonomic Nervous System Laboratory) is an integrated software suite that supports data visualization, artifact detection, data reduction, automated processing, and statistical analysis for a large range of autonomic, respiratory, and muscular measures. Analysis modules for cardiovascular (e.g., electrocardiogram, heart rate variability, blood pressure wave, pulse wave, and impedance cardiography), electrodermal (skin conductance level and responses), respiratory (respiratory pattern, timing, and volume variables, as well as capnography), and muscular (eye-blink startle, facial and bodily electromyography) systems are complemented by specialized modules (e.g., body temperature and accelerometry, cross-spectral analysis of respiratory and cardiac measures, signal averaging, and statistical analysis) and productivityenhancing features (batched processing, fully automatized analyses, and data management). ANSLAB also facilitates the analysis of long-term recordings from ambulatory assessment studies. The present article reviews several analysis modules included in ANSLAB and describes how these address some of the current needs and methodological challenges of psychophysiological science.