From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

Fahrenfort, Johannes J.; Driel, Joram van; Gaal, Simon van; Olivers, Christian N. L.

doi:10.3389/fnins.2018.00368

Cited by 139 publications

(181 citation statements)

References 49 publications

(82 reference statements)

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“…Using the ADAM toolbox (Fahrenfort, van Driel, van Gaal, & Olivers, 2018), we applied a classification algorithm to high-pass filtered data (0.5 Hz) for each participant using 10-fold cross validation. Using all electrodes, each dataset was split into 10 equally sized subsets.…”

Section: Frequency Taggingmentioning

confidence: 99%

A silent disco: Differential effects of beat-based and pattern-based temporal expectations on persistent entrainment of low-frequency neural oscillations

Bouwer¹,

Fahrenfort²,

Millard³

et al. 2020

Preprint

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Psychology, Vrije Universiteit Amsterdam (h.a.slagter@vu.nl). AbstractTemporal expectations (e.g., predicting "when") facilitate sensory processing, and are suggested to rely on entrainment of low frequency neural oscillations to regular rhythmic input.However, temporal expectations can be formed not only in response to a regular beat, such as in music ("beat-based" expectations), but also based on a predictable pattern of temporal intervals of different durations ("memory-based" expectations). Here, we examined the neural mechanisms underlying beat-based and memory-based expectations, by assessing EEG activity and behavioral responses during silent periods following rhythmic auditory sequences that allowed for beat-based or memory-based expectations, or had random timing. In Experiment 1 (N = 32), participants rated how well probe tones at various time points fitted the previous rhythm. Beat-based expectations affected fitness ratings for at least two beat-cycles, while the effects of memory-based expectations subsided after the first expected time point in the silence window. In Experiment 2 (N = 27), using EEG, we found a CNV following the final tones of memory-based and random, but not beat-based sequences, suggesting that climbing neuronal activity may specifically reflect memory-based expectations. Moreover, we found enhanced power in the EEG signal at the beat frequency for beat-based sequences both during listening and the silence. For memory-based sequences, we found enhanced power at a frequency inherent to the memory-based pattern only during listening, but not during the silence, suggesting that ongoing entrainment of low frequency oscillations may be specific to beatbased expectations. Finally, using multivariate pattern decoding on the raw EEG data, we could classify above chance from the silence which type of sequence participants had heard before.Together, our results suggest that beat-based and memory-based expectations rely on entrainment and climbing neuronal activity, respectively.

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Section: Frequency Taggingmentioning

confidence: 99%

A silent disco: Differential effects of beat-based and pattern-based temporal expectations on persistent entrainment of low-frequency neural oscillations

Bouwer¹,

Fahrenfort²,

Millard³

et al. 2020

Preprint

Self Cite

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“…(Figure 3). The ADAM-toolbox 632 was used on the Broadband and Rhythmic PSC time-courses with epochs from -100 ms to 400 ms 633 (Fahrenfort et al, 2018). Crucially, and for each individual component, we trained a linear discriminant 634 (LDA) classifier in one monkey and tested in a separate monkey for obtaining cross-individual 635 decodability of stimuli expectancy category, i.e.…”

Section: Cross-individual Decoding 628 629mentioning

confidence: 99%

Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex

Canales‐Johnson

Borges

Komatsu

et al. 2019

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Detection of statistical irregularities, measured as a prediction error response, is fundamental to the 47 perceptual monitoring of the environment. We studied whether prediction error response is generated 48 by neural oscillations or asynchronous neuronal firing. Electrocorticography (ECoG) was carried out 49 in three monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials 50 (LFP) recorded over the auditory cortex underwent spectral principal component analysis, which 51 decoupled broadband and rhythmic components of LFP signal. We found that broadband component 52 generated prediction error response, whereas none of the rhythmic components encoded statistical 53 irregularities of sounds. The broadband component displayed more stochastic, asymmetrical 54 multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We 55 thus conclude that the prediction error response is encoded by asynchronous neuronal populations, 56 defined by irregular dynamical states which, unlike oscillatory rhythms, appear to enable the neural 57 representation of auditory prediction error response. 58 59 60

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“…Overall classification 25 accuracy was the average accuracy of these 99 iterations. Moreover, we used a balanced accuracy 26 calculation as described in Fahrenfort et al (2018), where accuracy is calculated separated per 27 class and then averaged across classes. 28…”

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confidence: 99%

Current and future goals are represented in opposite patterns in object-selective cortex

Loon

Olivers

Fahrenfort

2018

Preprint

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1Adaptive behavior requires the separation of current from future behavioural goals in 2 working memory. We used fMRI of object-selective cortex to determine the 3 representational (dis)similarities of memory representations serving current and prospective 4 perceptual tasks. Human participants remembered different target objects for two 5 consecutive visual search tasks. A cue indicated whether an object should be looked for first 6 (making it currently relevant), or second (making it prospectively relevant). Prior to the first 7 search, category decoding for currently relevant targets was better than for prospectively 8 relevant targets, while representations were similar. During the first search, the prospective 9 target representation could also momentarily be decoded, but now current and prospective 10

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From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

Cited by 139 publications

References 49 publications

A silent disco: Differential effects of beat-based and pattern-based temporal expectations on persistent entrainment of low-frequency neural oscillations

A silent disco: Differential effects of beat-based and pattern-based temporal expectations on persistent entrainment of low-frequency neural oscillations

Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex

Current and future goals are represented in opposite patterns in object-selective cortex

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