Electrophysiological activation by masked primes: Independence of prime-related and target-related activities

Visual stimuli that are made invisible by metacontrast masking (primes) have a marked influence on behavioral and psychophysiological measures such as reaction time (RT) and the lateralized readiness potential (LRP). 4 experiments are reported that shed light on the effects that masked primes have on the LRP. Participants had a go-nogo task in which the prime was associated with 1 of 2 responses even if the target required participants to refrain from responding. To analyze the electrophysiological responses, we computed the LRP and applied an averaging method separating the activation due to the prime and the target. The results demonstrated that (a) masked primes activate responses even in a nogo situation, (b) this prime-related activation is independent of masking, (c) and is also independent of whether prime and target require the same responses (congruent condition) or different responses (incongruent condition)

Visual conscious perception could be grounded in a nonconscious sensorimotor domain

Visual conscious perception could be grounded in a nonconscious sensorimotor domain. Although invisible, information can be processed up to the level of response activation. Moreover, these nonconscious processes are modified by actual intentions. This notion bridges a gap in the theoretical framework of O'Regan &amp; Noë.</jats:p

Improving transfer rates in brain computer interfacing: A case study

In this paper we present results of a study on brain computer interfacing. We adopted an approach of Farwell &amp; Donchin [4], which we tried to improve in several aspects. The main objective was to improve the transfer rates based on offline analysis of EEG-data but within a more realistic setup closer to an online realization than in the original studies. The objective was achieved along two different tracks: on the one hand we used state-of-the-art machine learning techniques for signal classification and on the other hand we augmented the data space by using more electrodes for the interface. For the classification task we utilized SVMs and, as motivated by recent findings on the learning of discriminative densities, we accumulated the values of the classification function in order to combine several classifications, which finally lead to significantly improved rates as compared with techniques applied in the original work. In combination with the data space augmentation, we achieved competitive transfer rates at an average of 50.5 bits/min and with a maximum of 84.7 bits/min.