Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers

Das, Kishore Kumar; Giesbrecht, Barry; Eckstein, Miguel P.

doi:10.1016/j.neuroimage.2010.03.030

Cited by 76 publications

(62 citation statements)

References 46 publications

(54 reference statements)

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“…After preprocessing and transformation into CSD datasets, multivariate pattern classification analyses were performed Das et al, 2010;Blankertz et al, 2011). First, data from intact trials for all 63 electrodes were sorted into conditions, according to stimulus category or the participant's choice.…”

Section: Methodsmentioning

confidence: 99%

“…Recent studies have shown that choice outcomes can be predicted from brain activity before an overt response being made (Das et al, 2010;Bode et al, 2012). One controversial finding has been the existence of choice-related brain activity before the presentation of decision-relevant stimuli (Hesselmann et al, 2008a(Hesselmann et al, ,b, 2010.…”

Section: Introductionmentioning

confidence: 99%

“…Participants were unaware of the inclusion of these pure noise trials and continued to make object category decisions. We recorded 63-channel electroencephalography (EEG) and performed multivariate pattern classification analyses on the data (Haynes and Rees, 2006;Pereira et al, 2009;Das et al, 2010;Blankertz et al, 2011). This allowed us to decode the decision outcomes directly from brain activity in successive time steps throughout the trial.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Perceptual Decision Biases from Early Brain Activity

et al. 2012

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Perceptual decision making is believed to be driven by the accumulation of sensory evidence following stimulus encoding. More controversially, some studies report that neural activity preceding the stimulus also affects the decision process. We used a multivariate pattern classification approach for the analysis of the human electroencephalogram (EEG) to decode choice outcomes in a perceptual decision task from spatially and temporally distributed patterns of brain signals. When stimuli provided discriminative information, choice outcomes were predicted by neural activity following stimulus encoding; when stimuli provided no discriminative information, choice outcomes were predicted by neural activity preceding the stimulus. Moreover, in the absence of discriminative information, the recent choice history primed the choices on subsequent trials. A diffusion model fitted to the choice probabilities and response time distributions showed that the starting point of the evidence accumulation process was shifted toward the previous choice, consistent with the hypothesis that choice priming biases the accumulation process toward a decision boundary. This bias is reflected in prestimulus brain activity, which, in turn, becomes predictive of future decisions. Our results provide a model of how non-stimulus-driven decision making in humans could be accomplished on a neural level.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Perceptual Decision Biases from Early Brain Activity

et al. 2012

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“…The average peak for the first component was at 105 Ϯ 16.1 ms poststimulus, and for the second component was at 242 Ϯ 19.2 ms poststimulus. We concentrated on these two components for further analysis, as previous studies suggest that they reflect distinct processes (Johnson and Olshausen, 2003;Ohla et al, 2005;Pei et al, 2005;Tanskanen et al, 2008;Das et al, 2010). In particular, previous studies showing differential responses to global forms at later rather than early latencies suggest that latencies around the first component relate to visual form integration, while latencies around the second component relate to perceptual classification judgments.…”

Section: Eeg-informed Fmri Mapping Of Regions Of Interestmentioning

confidence: 99%

“…Therefore, similar to the analysis of the fMRI data, we used sensitive multivariate methods (i.e., pattern classification) for comparing EEG data before and after training. Similar to the fMR-metric functions, we generated EEG-metric functions Das et al, 2010) for each of the two EEG components (Fig. 5A).…”

Section: Learning-dependent Changes: Eeg-metric Functionsmentioning

confidence: 99%

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

Mayhew

Li²,

Kourtzi³

2012

J. Neurosci.

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Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learningdependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

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Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister

Springenberg

Fiederer

et al. 2017

Human Brain Mapping

2,272

2,128

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Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.

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Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers

Cited by 76 publications

References 46 publications

Predicting Perceptual Decision Biases from Early Brain Activity

Predicting Perceptual Decision Biases from Early Brain Activity

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

Deep learning with convolutional neural networks for EEG decoding and visualization

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