https://www.cell.com/trends/cognitive-sciences/pdf/S1364-6613(12)00243-4.pdf

Alpha-band oscillations, attention, and controlled access to stored information

Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

/pdf/the-steady-state-visual-evoked-potential-in-vision-research-123m7llkdr.pdf

The steady-state visual evoked potential in vision research: A review.

The current study aims at characterizing the mechanisms that allow humans to entrain the mind and body to incoming rhythmic sensory inputs in real time. We addressed this unresolved issue by examining the relationship between covert neural processes and overt behavior in the context of musical rhythm. We measured temporal prediction abilities, sensorimotor synchronization accuracy and neural entrainment to auditory rhythms as captured using an EEG frequency-tagging approach. Importantly, movement synchronization accuracy with a rhythmic beat could be explained by the amplitude of neural activity selectively locked with the beat period when listening to the rhythmic inputs. Furthermore, stronger endogenous neural entrainment at the beat frequency was associated with superior temporal prediction abilities. Together, these results reveal a direct link between cortical and behavioral measures of rhythmic entrainment, thus providing evidence that frequency-tagged brain activity has functional relevance for beat perception and synchronization.

/pdf/individual-differences-in-rhythmic-cortical-entrainment-466ohnub18.pdf

Individual Differences in Rhythmic Cortical Entrainment Correlate with Predictive Behavior in Sensorimotor Synchronization

Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

Emotions Recognition Using EEG Signals: A Survey

http://repositori.uvic.cat/bitstream/handle/10854/2074/artconlli_a2012_latchoumane_charles_multiway_array.pdf?sequence=1

Multiway array decomposition analysis of EEGs in Alzheimer's disease

Background
oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task.

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group differences and within-subject variability. We found that ICA diminished Leave-One-Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group difference. More interestingly, ICA reduced the inter-subject variability within each group (?= 2.54 in the ? range before ICA, ?= 1.56 after, Bartlett p = 0.046 after Bonferroni correction). Additionally, we present a method to limit the impact of human error (? 13.8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These findings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

/pdf/improving-the-quality-of-eeg-data-in-patients-with-alzheimer-4h5ji1x82h.pdf

Improving the Quality of EEG Data in Patients with Alzheimer's Disease Using ICA

/pdf/bump-time-frequency-toolbox-a-toolbox-for-time-frequency-2l1nyhllha.pdf

Monique Maurice

Papers

Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

Multiway array decomposition analysis of EEGs in Alzheimer's disease

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Improving the Quality of EEG Data in Patients with Alzheimer's Disease Using ICA

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals