[Eeglablist] Single trial analysis

[Tarik S Bel-Bahar]

Hello Gian, one other recent article in case you have not seen it, from the
eeglab team:

Grand average ERP-image plotting and statistics: A method for comparing
variability in event-related single-trial EEG activities across subjects
and conditions



On Wed, Oct 11, 2017 at 12:45 PM, Tarik S Bel-Bahar <tarikbelbahar at gmail.com
> wrote:

> Hello Gian, some quick notes below, best wishes!
>
>
> ************BEGIN NOTES FOR GIAN
>
> There are several papers out there on single-trial metrics, both in
> mainstream EEG journals, imaging journals, methods journals, as well as
> IEEE/BCI/neurorehab/neuro-engineering journals. This is an important
> topic as the field ihas been moving away (more quickly in recent years)
> from "averaged" responses to understanding variability across trials, the
> validity of using single trials, etc.. Take a good look through those
> publications for good ideas. If you have trouble finding those, just look
> for any articles in last 10 years that mention single-trial and EEG in
> their title or abstract, and review them all. Then you can become an expert
> on what's been done, what's not been done, and contribute to this area of
> EEG methods/metrics. Note that the BCI field has also dealt extensively
> with working with single-trials and there are great reviews out there
> summarizing the efficacy of those methods, and the accuracy of various
> machine/classifier approaches to working with BCI-related protocols.
>
> I've include a range of mostly recent papers (including several reviews
> focused on the single-trial topic) below. You can find them all easily on
> Google Scholar. Note there are many other papers that I am not listing
> here. When you have developed a good list of ideas/methods/etc... please
> consider sharing them with the list as it will also help other users make
> benefit of the single-trial brain dynamics, and make informed decisions.
>
> Within eeglab, you can certainly pull out single trial metrics, and you
> already have important issues/caveats in mind. As you know eeglab
> emphasizes the power and usefulness of ICA, so one thing you can do is stay
> in ICA space, use good neural IC components, and focus on IC-resolved
> single-trial metrics. In fact the developers of eeglab had a strong focus
> on making it easier to visualize and work with single-trial brain dynamics,
> as there's where some of the most interesting "action" happens. In other
> words, humans naturally function in terms of single-trials, it's only
> science that's needed to start with averaged responses (though, again, this
> has been changing for at least the last two decades). When you start your
> trials of single-trial analyses with eeglab, you can send more focused and
> specific questions to the list, especially if you bump into problems of
> implementation in terms of the eeglab gui and scripting your methods.
>
> I've also seen some paper that essentially drop all ICs except the one of
> interest, and then looks at the rebuilt channel-level data based on that.
> This is one way of many to "single-out" the activity of interest at each
> trial. Overall, you would want to pull/use single-trials that really match
> some prototype ERP, but you would have to deal with the fact that ERPs are
> usually averaged across trials. Explore the ERPimage technique in eeglab at
> least to get a sense of how you can visualize data across all your trials,
> including sorting them in various ways. Another set of issues one would
> have to deal with is cleanliness of the data, method of preprocessing,
> individual differences in signal quality, variation in ERPs across single
> trials within each participant, and the validity of the protocol you have
> used to generate the ERPs in question, including the quality of your
> specific implementation of that protocol.
>
>
> Best practice for single-trial detection of event-related potentials:
> Application to brain-computer interfaces
>
> Simultaneous recording of MEG, EEG and intracerebral EEG during visual
> stimulation: from feasibility to single-trial analysis
>
> The effects of reward magnitude on reward processing: An averaged and
> single trial event-related potential study
>
> Exploiting the intra-subject latency variability from single-trial
> event-related potentials in the P3 time range: A review and comparative
> evaluation of methods
>
> Similar sound intensity dependence of the N1 and P2 components of the
> auditory ERP: averaged and single trial evidence
>
> Single-trial analysis and classification of ERP components—a tutorial
>
> Single-trial normalization for event-related spectral decomposition
> reduces sensitivity to noisy trials
>
> A Bayesian method to estimate single-trial event-related potentials with
> application to the study of the P300 variability
>
> Functional source separation improves the quality of single trial visual
> evoked potentials recorded during concurrent EEG-fMRI
>
> The superior temporal sulcus and the N170 during face processing: single
> trialanalysis of concurrent EEG–fMRI
>
> Attentional selection in a cocktail party environment can be decoded from
> single-trial EEG
>
> Space-by-time decomposition for single-trial decoding of M/EEG activity
>
> Neural Activity Elicited by a Cognitive Task can be Detected in
> Single-Trials with Simultaneous Intracerebral EEG-fMRI Recordings
>
> A Change-point Analysis Method for Single-trial Study of Simultaneous
> EEG-fMRI of Auditory/Visual Oddball Task
>
> Single-trial analysis of readiness potentials for lower limb exoskeleton
> control
>
> How attention influences perceptual decision making: Single-trial
> EEGcorrelates of drift-diffusion model parameters
>
> Predicting EEG single trial responses with simultaneous fMRI and relevance
> vector machine regression
>
> Individual differences in human auditory processing: Insights from
> single-trialauditory midbrain activity in an animal model
>
> A pipeline of spatio-temporal filtering for predicting the laterality of
> self-initiated fine movements from single trial readiness potentials
>
> Single-trial detection of somatosensory evoked potentials by probabilistic
> independent component analysis and wavelet filtering
>
> Selectivity of N170 for visual words in the right hemisphere: Evidence
> fromsingle‐trial analysis
>
> Single-trial event-related potential extraction through one-unit
> ICA-with-reference
>
> A single trial analysis of EEG in recognition memory: Tracking the neural
> correlates of memory strength
>
> Interpretable deep neural networks for single-trial EEG classification
>
> Machine learning for real-time single-trial EEG-analysis: from
> brain–computer interfacing to mental state monitoring
>
> Exploring time-and frequency-dependent functional connectivity and brain
> networks during deception with single-trial event-related potentials
>
> Trial‐by‐trial co‐variation of pre‐stimulus EEG alpha power and
> visuospatial bias reflects a mixture of stochastic and deterministic effects
>
> The Pavlovian craver: Neural and experiential correlates of single
> trialnaturalistic food conditioning in humans
>
> Single-trial EEG classification of motor imagery using deep convolutional
> neural networks
>
> Open Access Dataset for EEG+ NIRS Single-Trial Classification
>
> Pre-trial EEG-based single-trial motor performance prediction to enhance
> neuroergonomics for a hand force task
>
> Optimal spatial filtering of single trial EEG during imagined hand movement
>
> Using single-trial EEG to predict and analyze subsequent memory
>
> Classifying single trial EEG: Towards brain computer interfacing
>
> A comparison of single-trial EEG classification and EEG-informed fMRI
> across three MR compatible EEG recording systems
>
> Toward FRP-Based Brain-Machine Interfaces—Single-Trial Classification of
> Fixation-Related Potentials
>
> Spatio-spectral filters for improving the classification of single trial
> EEG
>
> Sliding HDCA: single-trial EEG classification to overcome and quantify
> temporal variability
>
> Spatiotemporal representations of rapid visual target detection: a
> single-trial eegclassification algorithm
>
> The P600-as-P3 hypothesis revisited: Single-trial analyses reveal that the
> late EEG positivity following linguistically deviant material is reaction
> time aligned
>
> Single Trial EEG Patterns for the Prediction of Individual Differences in
> Fluid Intelligence
>
> Single-trial EEG classification using logistic regression based on
> ensemble synchronization
>
> RSTFC: A novel algorithm for spatio-temporal filtering and classification
> ofsingle-trial EEG
>
> Neurological classifier committee based on artificial neural networks and
> support vector machine for single-trial EEG signal decoding
>
>
>
>
>
>
>
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