&amp;#x03B1;-modulation induced by covert attention shifts as a new input modality for EEG-based BCIs

Schmidt, Nico M.; Blankertz, Benjamin; Treder, Matthias S.

doi:10.1109/icsmc.2010.5641967

Cited by 8 publications

(16 citation statements)

References 17 publications

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“…This increase would reflect an inhibitory process of the information coming from the controlateral hemi-field, thus allowing the allocation of more resources to the hemi-field of interest. Schmidt et al 23 have demonstrated in an EEG study that covert attention shifts to different target locations yielded distinctive topographical distributions of posterior α activity, paving the way for a NF paradigm. Consequently, the neurophysiological patterns underlying CVSA have been explored via the modulation of both the brain responses elicited by external visual stimuli 24 , and via the self-generated modulation of the activity in the visual cortex in a fully endogenous way (i.e., not depending on external stimuli) 25,26 .…”

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confidence: 99%

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Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Jeunet

Tonin

Albert³

et al. 2020

Sci Rep

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Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to selfregulate specific brain rhythms and consequently improve their performances. Here, we focused on soccer goalkeepers' Covert Visual Spatial Attention (CVSA) abilities, which are essential for these athletes to reach high performances. We looked for Electroencephalography (EEG) markers of CVSA usable for virtual reality-based NF training procedures, i.e., markers that comply with the following criteria: (1) specific to CVSA, (2) detectable in real-time and (3) related to goalkeepers' performance/ expertise. Our results revealed that the best-known EEG marker of CVSA-increased α-power ipsilateral to the attended hemi-field-was not usable since it did not comply with criteria 2 and 3. Nonetheless, we highlighted a significant positive correlation between athletes' improvement in CVSA abilities and the increase of their α-power at rest. While the specificity of this marker remains to be demonstrated, it complied with both criteria 2 and 3. This result suggests that it may be possible to design innovative ecological training procedures for goalkeepers, for instance using a combination of NF and cognitive tasks performed in virtual reality.

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confidence: 99%

“…Both sessions had the same structure. The goalkeepers performed a CVSA task based on the protocol of Schmidt et al 23 , during which their EEG activity was recorded. They also completed a Multiple Object Tracking (MOT) test at the beginning and end of each session.…”

mentioning

confidence: 99%

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Jeunet

Tonin

Albert³

et al. 2020

Sci Rep

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“…For the covert orienting (EEG) dataset, the method performed as-good or better than previously-published frequency-based decoding ( [26]) at the group level ( Figure 3A,B), but the negatively-trending correlation in performance at the individual level ( Figure 3C)suggests that these approaches are sensitive to different signal properties and might benefit from being used together. By remapping the conic weights to spatial locations, we showed how our method reveals retinotopic organization of posterior channels during covert attention.…”

Section: Discussionmentioning

confidence: 66%

“…Attention In the next experiment, we consider the method's application to an EEG dataset ( [26], [14]) recorded during a spatial covert-orienting task. During the task a briefly presented central cue indicated where a subsequent taskrelevant target would appear with 80% accuracy.…”

Section: The Conic Methods Produces Instantaneous Decoding Of Covert Smentioning

confidence: 99%

“…Previous results using this dataset have demonstrated that the spatial distribution of alpha power (across channels) may be used to determine to which location subjects are covertly orienting. These previous analyses ( [26]) were performed pair-wise using L1-regularized logistic regression for alpha-band power in parieto-occipital electrodes and the authors presented the best pair accuracy for each subject (74.6 ± 2.3%) as well as the number of significantly classified pairs per subject: (3.5 ± 2.67) out of a total of 15. In our comparison, we use all trials with delays of at least 1300ms and only consider data after the first 250ms post-cue.…”

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Geometric classification of brain network dynamics via conic derivative discriminants

Singh

Braver

Ching

2017

Preprint

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Over the past decade, pattern decoding techniques have granted neuroscientists improved anatomical specificity in mapping neural representations associated with function and cognition. Dynamical patterns are of particular interest, as evidenced by the proliferation and success of frequency domain methods that reveal structured spatiotemporal rhythmic brain activity. One drawback of such approaches, however, is the need to estimate spectral power, which limits the temporal resolution of classification. We propose an alternative method that enables classification of dynamical patterns with high temporal fidelity. The key feature of the method is a conversion of time-series into their temporal derivatives. By doing so, dynamically-coded information may be revealed in terms of geometric patterns in the phase space of the derivative signal. We derive a geometric classifier for this problem which simplifies into a straightforward calculation in terms of covariances. We demonstrate the relative advantages and disadvantages of the technique with simulated data and benchmark its performance with an EEG dataset of covert spatial attention. By mapping the weights anatomically we reveal a retinotopic organization of covert spatial attention. We especially highlight the ability of the method to provide strong group-level classification performance compared to existing benchmarks, while providing information that is synergistic to classical spectral-based techniques. The robustness and sensitivity of the method to noise is also examined relative to spectral-based techniques. The proposed classification technique enables decoding of dynamic patterns with high temporal resolution, performs favorably to benchmark methods, and facilitates anatomical inference.

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Geometric classification of brain network dynamics via conic derivative discriminants

Singh

Braver

Ching

2018

Journal of Neuroscience Methods

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α-modulation induced by covert attention shifts as a new input modality for EEG-based BCIs

Cited by 8 publications

References 17 publications

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Geometric classification of brain network dynamics via conic derivative discriminants

Geometric classification of brain network dynamics via conic derivative discriminants

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