Decoding covert shifts of attention induced by ambiguous visuospatial cues

Simple and unambiguous visual cues (e.g., an arrow) can be used to trigger covert shifts of visual attention away from the center of gaze. The processing of visual stimuli is enhanced at the attended location. Covert shifts of attention modulate the power of cerebral oscillations in the alpha band over parietal and occipital regions. These modulations are sufficiently robust to be decoded on a single trial basis from electroencephalography (EEG) signals. It is often assumed that covert attention shifts are under voluntary control, and that they also occur in more natural and complex environments, but there is no direct evidence to support this assumption. We address this important issue by using random-dot stimuli to cue one of two opposite locations, where a visual target is presented. We contrast two conditions, one in which the random-dot motion is predictive of the target location, and the other, in which it provides ambiguous information. Behavioral results show attention shifts in anticipation of the visual target, in both conditions. In addition, using the common spatial patterns (CSPs) algorithm, we extract EEG power features in the alpha-band (around 10 Hz) that best discriminate the attended location in single trials. We obtain a significant decoding accuracy in 7/10 subjects using a cross-validation procedure applied in the predictive condition. Interestingly, similar accuracy (significant in 5/10 subjects) is obtained when the CSPs trained in the predictive condition are tested in the ambiguous condition. In agreement with this result, we find that the CSPs show very similar topographies in both conditions. These results shed a new light on the behavioral and EEG correlates of visuospatial attention in complex visual environments. This study demonstrates that alpha-power features could be used in brain–computer interfaces to decode covert attention shifts in an environment containing ambiguous spatial information.

[1]  M. Kawato,et al.  Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex. , 2005, Brain research. Cognitive brain research.

[2]  Christoph M. Michel,et al.  A bias for posterior α-band power suppression versus enhancement during shifting versus maintenance of spatial attention , 2009, NeuroImage.

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Manuel Schabus,et al.  A shift of visual spatial attention is selectively associated with human EEG alpha activity , 2005, The European journal of neuroscience.

[5]  Stephen J. Anderson,et al.  Attentional modulation of oscillatory activity in human visual cortex , 2003, NeuroImage.

[6]  C. Braun,et al.  Hand Movement Direction Decoded from MEG and EEG , 2008, The Journal of Neuroscience.

[7]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[8]  Jane-Ling Wang,et al.  Spontaneous Neural Fluctuations Predict Decisions to Attend , 2014, Journal of Cognitive Neuroscience.

[9]  F. Smulders,et al.  Varieties of attention in neutral trials: linking RT to ERPs and EEG frequencies. , 2006, Psychophysiology.

[10]  C Christine Camblin,et al.  Isolating the internal in endogenous attention. , 2010, Psychophysiology.

[11]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[12]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[13]  G. V. Simpson,et al.  Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex , 2000, The Journal of Neuroscience.

[14]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[15]  Jason Farquhar,et al.  Exploring the Impact of Target Eccentricity and Task Difficulty on Covert Visual Spatial Attention and Its Implications for Brain Computer Interfacing , 2013, PloS one.

[16]  Jean-Philippe Lachaux,et al.  Shifting visual attention away from fixation is specifically associated with alpha band activity over ipsilateral parietal regions. , 2011, Psychophysiology.

[17]  G. Thut,et al.  Mechanisms of selective inhibition in visual spatial attention are indexed by α‐band EEG synchronization , 2007, The European journal of neuroscience.

[18]  Stephen J. Anderson,et al.  Elsevier Editorial System(tm) for Brain Research Manuscript Draft Response Letter Reviewer Number 1 Attentional Changes in Pre-stimulus Oscillatory Activity within Early Visual Cortex Are Predictive of Human Visual Performance , 2007 .

[19]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[20]  Marcel A. J. van Gerven,et al.  Lateralized responses during covert attention are modulated by target eccentricity , 2011, Neuroscience Letters.

[21]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[22]  M. Posner,et al.  Orienting of Attention* , 1980, The Quarterly journal of experimental psychology.

[23]  B. Dosher,et al.  PSYCHOLOGICAL SCIENCE Research Article NOISE EXCLUSION IN SPATIAL ATTENTION , 2022 .

[24]  L Tonin,et al.  An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation , 2013, Journal of neural engineering.

[25]  Jessica J. Green,et al.  The role of temporal predictability in the anticipatory biasing of sensory cortex during visuospatial shifts of attention. , 2010, Psychophysiology.

[26]  Junya Fujisawa,et al.  Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  M. V. Gerven,et al.  Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces , 2009, Journal of Neuroscience Methods.

[28]  Adaptive and warning displays with Brain-Computer Interfaces: Enhanced visuospatial attention performance , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[29]  Á. Pascual-Leone,et al.  α-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection , 2006, The Journal of Neuroscience.

[30]  T. Heskes,et al.  Covert attention allows for continuous control of brain–computer interfaces , 2010, The European journal of neuroscience.

[31]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

[32]  Ali Bahramisharif,et al.  Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention , 2011, Journal of NeuroEngineering and Rehabilitation.

[33]  Jessica J. Green,et al.  Electrical Neuroimaging Reveals Timing of Attentional Control Activity in Human Brain , 2008, PLoS Biology.

[34]  C. Tallon-Baudry,et al.  Neural Dissociation between Visual Awareness and Spatial Attention , 2008, The Journal of Neuroscience.

[35]  A. Watson,et al.  Quest: A Bayesian adaptive psychometric method , 1983, Perception & psychophysics.

[36]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[37]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, IEEE Transactions on Biomedical Engineering.