EEG miniaturization limits for stimulus decoding with EEG sensor networks.

Objective. Unobtrusive electroencephalography (EEG) monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance.Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding.Main results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm.Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.

Long-term impact of prenatal exposure to chemotherapy on executive functioning: An ERP study.

OBJECTIVE: This study examines the long-term impact of prenatal exposure to chemotherapy on executive functioning and the contribution of late-prematurity to this effect, using event-related potentials. METHODS: Mothers of the prenatal-exposed children (n = 20) were diagnosed with cancer and received chemotherapeutic treatment during pregnancy. We recruited healthy controls (n = 20) who were matched on a 1:1 ratio regarding prematurity, age and sex. We assessed executive functioning at the age of nine, using two event-related potential paradigms: a Go/Nogo paradigm to investigate processes of response inhibition and conflict monitoring, as well as a Posner paradigm to investigate spatial attention. RESULTS: Lower potentials were found in prenatal-exposed children compared to controls in the Go/Nogo P3 and Posner positive slow wave. Moreover, prenatal-exposed children responded slower on the Posner paradigm compared to controls (p < .033), with more incorrect responses (p = .023). In the control group, the N2 Go/Nogo wave was more pronounced in children born after a longer gestation. CONCLUSIONS: This is the first study that demonstrates an effect of prenatal exposure to chemotherapy on the development of executive functioning, not limited to the effect of late-prematurity. SIGNIFICANCE: This study emphasizes the necessity of a long-term follow-up of prenatal-exposed children to re-inform clinical practice on the costs and benefits of late-premature induction over treatment during pregnancy.

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks.

OBJECTIVE: In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. APPROACH: We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. MAIN RESULTS: A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. SIGNIFICANCE: Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.

OBJECTIVE: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. APPROACH: We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. MAIN RESULTS: The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. SIGNIFICANCE: The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

The quest for single trial correlations in multimodal EEG-fMRI data.

In the past decade, technological advances have made it possible to reliably measure brain activity using simultaneous EEG-fMRI recordings inside an MR scanner. The main challenge then became to investigate the coupling between the EEG and fMRI signals in order to benefit from the simultaneously integrated temporal and spatial resolution. Although it is crucial to know when features in EEG and fMRI are expected to correlate with each other before the identification of common sources from multimodal data is possible, it is still a matter of debate. In this study, we address this question by analysing EEG and fMRI data separately from a face processing task. We show that we are able to reliably estimate single trial (ST) dynamics of face processing in EEG and fMRI data separately in four subjects. However, no correlation is found between the modalities. This implies that in this task modality-specific information is larger than the information that is shared by the modalities.