Tracking momentary fluctuations in human attention with a cognitive brain-machine interface

Chinchani, Abhijit; Paliwal, Siddharth; Ganesh, Suhas; Chandrasekhar, Vishnu; Yu, Byron M.; Sridharan, Devarajan

doi:10.1038/s42003-022-04231-w

Cited by 9 publications

(4 citation statements)

References 80 publications

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“…SSVEPs, on the other hand, have been used extensively in BMI applications to decode the focus of attention based on stimulus location or feature using human EEG (Min et al, 2016;Chinchani et al, 2022). Although the underlying neural mechanisms might be different; since alpha and SSVEP both show comparable attentional modulation in EEG, a combination of both the neural measures may be able to decode attended location better than a single measure.…”

Section: Bmi Applications For Decoding Focus Of Attentionmentioning

confidence: 99%

“…However, this comparison is important for two reasons. First, although earlier brain-machine interfacing (BMI) studies mainly focused on motor decoding (Bansal et al, 2012;Hwang and Andersen, 2013), recent studies have attempted to decode the focus of attention (De Sousa et al, 2021;Prakash et al, 2021;Chinchani et al, 2022) as well. For such applications, it is important to compare how attention modulates various neural measures.…”

Section: Introductionmentioning

confidence: 99%

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Alpha and SSVEP power outperform Gamma power in capturing Attentional Modulation in Human EEG

Das

Nandi

Ray

2023

Preprint

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The effect of visual attention has been extensively studied using various techniques such as macaque neurophysiology that yields spikes and local field potential (LFP), and human electro/magneto encephalogram (EEG/MEG). Attention typically suppresses power at low frequencies such as alpha band (8-12 Hz) and increases power in gamma band (>30 Hz) in brain signals. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state visually evoked potential (SSVEP), whose power also increases with attention. However, effectiveness of these various neural measures in capturing attentional modulation is unknown since the stimuli and task paradigms vary widely across these studies. In a recent macaque neurophysiology study with flickering stimuli, we found that the effect of attention was more salient in the gamma band and beyond of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to male (N=15) and female (N=11) subjects, allowing us to compare attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed a negligible effect of attention although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features depending on recording modality are used for decoding attention, and also provide clues about the spatial scales of neural mechanisms underlying attention.Significance StatementVarious neural measures such as alpha and gamma band power or SSVEP power capture signatures of visual attention. A systematic comparison of their effectiveness in capturing attentional modulation is important for understanding different neural computations underlying attention and for developing brain-machine-interfaces (BMIs) that can decode the focus of attention. Since stimulus configuration and task paradigms vary widely across visual attention studies, we recorded all the relevant neural signals during an attention task under similar stimulus and behavioral conditions in human EEG. In contrast to invasive recordings in which gamma outperforms other measures, our results show that in human EEG, attentional modulation of alpha and SSVEP are comparable, and non-foveal stimuli produce weak gamma which is not well modulated by attention.

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Section: Bmi Applications For Decoding Focus Of Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alpha and SSVEP power outperform Gamma power in capturing Attentional Modulation in Human EEG

Das

Nandi

Ray

2023

Preprint

View full text Add to dashboard Cite

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“…Consequently, we transiently disconnected the feedforward connections from the VC to the HC during the maintenance epoch, thereby rendering the HC mnemonic information immune to disruption by the distractor upon its presentation. Finally, based on previous reports of competitive visual inhibition across the hemifield [39][40][41][42][43] , we also modeled topographic, cross-hemispheric inhibitory connections between the VC attractors in each hemisphere (Fig. 5A With this model, we sought to replicate the attractive and repulsive biases observed experimentally.…”

Section: An Attractor Model Explains Distractor-induced Biases In Vis...mentioning

confidence: 99%

Distractors induce space-specific neural biases in visual working memory

Gupta,

Sridharan

2023

Preprint

Self Cite

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Information in working memory is remarkably resilient to distraction. Yet, recent evidence suggests that distractors containing task-relevant features can disrupt working memory by inducing subtle biases in mnemonic representations. With multivariate decoding of human electroencephalography recordings, we show that temporally unpredictable distractors produce spatially-antagonistic mnemonic biases, across the visual hemifields. Grating distractors produced either an attractive or a repulsive mnemonic bias – a shift in the neural representation of the memorandum toward or away from the distractor’s orientation – depending, respectively, on whether the distractor appeared in the same hemifield as the memorandum, or opposite to it. Behavioral biases closely tracked these neural effects. We devised a two-tier ring attractor model with cross-hemifield inhibition, which comprehensively explains how the distractor’s timing, encoding strength, and input gating control these mnemonic biases. Our results provide a mechanistic account of distractor-induced biases, across space and time, in visual working memory.Lay SummaryWorking memory – the capacity to momentarily store important items or events – is remarkably robust to distraction. Yet, when a salient distractor with features resembling the memorized items appears, it can subtly alter (bias) working memory. Applying state-of-the-art neural decoding to human electrophysiological data, we show the such distractor biases are space-specific. The neural memory representation begins to resemble the distractor (attractive bias) when the distractor appears at the same location as the memory items, but becomes less like the distractor (repulsive bias) when the distractor appears further away. We tie together these experimental findings with a simple, computational model. The findings enhance our understanding of neural mechanisms underlying working memory and may be relevant for cognitive therapies addressing memory deficits.

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“…Attentional awareness of visual stimuli fluctuates with the phase of neural activity at stimulus onset (8)(9)(10)(11)(12)(13)(14)(15); choice outcomes during perceptual decision making track the underlying parietal delta and alpha rhythms (16,17); and the strength of memory formation waxes and wanes with the phase of spontaneous gamma oscillations across the visual cortex (18). Behavioural periodicities have been reported for a myriad of tasks (19)(20)(21)(22), in humans and animal studies (21,23), and across sensory systems (20,(24)(25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Just a phase? Causal probing reveals spurious phasic dependence of sensory perception

Vinao-Carl,

Gal-Shohet,

Rhodes

et al. 2023

Preprint

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For over a decade, electrophysiological studies have reported correlations between sensory perception and the phase of spontaneous pre-stimulus brain oscillations. To date, these findings have been interpreted as evidence that the brain uses neural oscillations to sample and predict upcoming stimuli. Yet, evidence from simulations have shown that analysis artefacts could also lead to spurious pre-stimulus oscillations that appear to predict future brain responses. To address this discrepancy, we conducted an experiment in which visual stimuli were presented timed to specific phases of spontaneous alpha and theta oscillations. This allowed us to causally probe the role of ongoing neural activity in visual processing independent of the stimulus-evoked dynamics. Our findings did not support a causal link between spontaneous alpha / theta rhythms and behaviour. However, spurious correlations between theta phase and behaviour emerged offline using gold-standard acausal time-frequency analyses. These findings demonstrate that care should be taken when inferring causal relationships between neural activity and behaviour using acausal analyses.HIGHLIGHTSWe causally probed the role of spontaneous EEG rhythms in visual attention via phase-locking.Theta phase predicted behaviour offline, but cues presented in real-time with theta had no effect on behaviour.Spurious theta rhythmic sampling is an artefact of the evoked potential and acausal filtering.ecHT accurately computes the phase in real-time and mitigates erroneous phase-behaviour correlations.

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Tracking momentary fluctuations in human attention with a cognitive brain-machine interface

Cited by 9 publications

References 80 publications

Alpha and SSVEP power outperform Gamma power in capturing Attentional Modulation in Human EEG

Alpha and SSVEP power outperform Gamma power in capturing Attentional Modulation in Human EEG

Distractors induce space-specific neural biases in visual working memory

Just a phase? Causal probing reveals spurious phasic dependence of sensory perception

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