Purpose in life predicts better emotional recovery from negative stimuli

Purpose in life predicts both health and longevity suggesting that the ability to find meaning from life’s experiences, especially when confronting life’s challenges, may be a mechanism underlying resilience. Having purpose in life may motivate reframing stressful situations to deal with them more productively, thereby facilitating recovery from stress and trauma. In turn, enhanced ability to recover from negative events may allow a person to achieve or maintain a feeling of greater purpose in life over time. In a large sample of adults (aged 36-84 years) from the MIDUS study (Midlife in the U.S., http://www.midus.wisc.edu/), we tested whether purpose in life was associated with better emotional recovery following exposure to negative picture stimuli indexed by the magnitude of the eyeblink startle reflex (EBR), a measure sensitive to emotional state. We differentiated between initial emotional reactivity (during stimulus presentation) and emotional recovery (occurring after stimulus offset). Greater purpose in life, assessed over two years prior, predicted better recovery from negative stimuli indexed by a smaller eyeblink after negative pictures offset, even after controlling for initial reactivity to the stimuli during the picture presentation, gender, age, trait affect, and other well-being dimensions. These data suggest a proximal mechanism by which purpose in life may afford protection from negative events and confer resilience is through enhanced automatic emotion regulation after negative emotional provocation.

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

FORCe: fully online and automated artifact removal for brain-computer interfacing

A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing. The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g. electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged auto-mutual information clustering (LAMIC) and Fully automated statistical thresholding (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.