Crossing the hands disrupts tactile spatial attention but not motor attention: Evidence from event-related potentials

2012 ◽  
Vol 50 (9) ◽  
pp. 2303-2316 ◽  
Author(s):  
Elena Gherri ◽  
Bettina Forster
2020 ◽  
Author(s):  
Xiangfei Hong ◽  
Ke Bo ◽  
Sreenivasan Meyyapan ◽  
Shanbao Tong ◽  
Mingzhou Ding

AbstractEvent-related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The commonly applied univariate ERP approach, however, has left important questions inadequately answered. Here, we addressed two questions by applying multivariate pattern classification to multichannel ERPs in two spatial-cueing experiments (N = 56 in total): (1) impact of cueing strategies (instructional vs. probabilistic) and (2) neural and behavioral effects of individual differences. Following the cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (∼80 ms) than in probabilistic cueing (∼160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attentional set. A similar temporal sequence was also found for target-related processing (cued targets vs. uncued targets), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments, individuals with higher decoding accuracy during ∼460-660 ms post-cue showed higher magnitude of attentional modulation of target-evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to better target processing. During target processing, individual difference in decoding accuracy was positively associated with behavioral performance (reaction time), suggesting that stronger selection of task-relevant information leads to better behavioral performance. Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that are not possible with the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.


1987 ◽  
Vol 24 (2) ◽  
pp. 153-162 ◽  
Author(s):  
Kimmo Alho ◽  
Nandor Donauer ◽  
Petri Paavilainen ◽  
Kalevi Reinikainen ◽  
Mikko Sams ◽  
...  

1999 ◽  
Vol 354 (1387) ◽  
pp. 1135-1144 ◽  
Author(s):  
Scott Makeig ◽  
Marissa Westerfield ◽  
Jeanne Townsend ◽  
Tzyy-Ping Jung ◽  
Eric Courchesne ◽  
...  

Spatial visual attention modulates the first negative–going deflection in the human averaged event–related potential (ERP) in response to visual target and non–target stimuli (the N1 complex). Here we demonstrate a decomposition of N1 into functionally independent subcomponents with functionally distinct relations to task and stimulus conditions. ERPs were collected from 20 subjects in response to visual target and non–target stimuli presented at five attended and non–attended screen locations. Independent component analysis, a new method for blind source separation, was trained simultaneously on 500 ms grand average responses from all 25 stimulus–attention conditions and decomposed the non–target N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetrical component pair (N1a R and N1a L ) was evoked by the left and right visual field stimuli, respectively. Component N1a R peaked ca. 9 ms earlier than N1a L . Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. The amplitudes of these components were not reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatio–temporally overlapping bilateral component (N1b) that peaked at ca. 180 ms and was strongly enhanced by attention. Stimuli presented at unattended locations evoked a fourth component (P2a) peaking near 240 ms. A fifth component (P3f) was evoked only by targets presented in either visual field. The distinct response patterns of these components across the array of stimulus and attention conditions suggest that they reflect activity in functionally independent brain systems involved in processing attended and unattended visuospatial events.


2011 ◽  
Vol 23 (1) ◽  
pp. 238-246 ◽  
Author(s):  
Søren K. Andersen ◽  
Sandra Fuchs ◽  
Matthias M. Müller

We investigated mechanisms of concurrent attentional selection of location and color using electrophysiological measures in human subjects. Two completely overlapping random dot kinematograms (RDKs) of two different colors were presented on either side of a central fixation cross. On each trial, participants attended one of these four RDKs, defined by its specific combination of color and location, in order to detect coherent motion targets. Sustained attentional selection while monitoring for targets was measured by means of steady-state visual evoked potentials (SSVEPs) elicited by the frequency-tagged RDKs. Attentional selection of transient targets and distractors was assessed by behavioral responses and by recording event-related potentials to these stimuli. Spatial attention and attention to color had independent and largely additive effects on the amplitudes of SSVEPs elicited in early visual areas. In contrast, behavioral false alarms and feature-selective modulation of P3 amplitudes to targets and distractors were limited to the attended location. These results suggest that feature-selective attention produces an early, global facilitation of stimuli having the attended feature throughout the visual field, whereas the discrimination of target events takes place at a later stage of processing that is only applied to stimuli at the attended position.


Author(s):  
Gokhan Altan ◽  
Gulcin Inat

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.


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