Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research.

Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.

Decreased prefrontal connectivity parallels cognitive fatigue-related performance decline after sleep deprivation. An optical imaging study.

Fatigue induced by sustained cognitive demands often entails decreased behavioural performance and the unavailability of brain resources, either due to reduced levels or impaired access. In the present study, we investigated the neural dynamics underlying preserved behavioural performance after inducing cognitive fatigue (CF) in a sleep deprivation (SD) condition in which resources are naturally compromised. Using functional near infrared spectroscopy (fNIRS), we recorded cortical brain activity during task-related CF induction in the evening, in the middle of the night and early in the morning. Although cortical oxygenation similarly increased over the 3 sessions, decreased intra-hemispheric connectivity between left anterior frontal and frontal areas paralleled a sudden drop in task performance in the early morning. Our data indicate that decreased sustained attention after the induction of cognitive fatigue in a situation of high sleep pressure results from impaired connectivity between left prefrontal cortical areas rather than from a mere modulation in brain resources.

Evoked and oscillatory EEG activity differentiates language discrimination in young monolingual and bilingual infants.

Language discrimination is one of the core differences between bilingual and monolingual language acquisition. Here, we investigate the earliest brain specialization induced by it. Following previous research, we hypothesize that bilingual native language discrimination is a complex process involving specific processing of the prosodic properties of the speech signal. We recorded the brain activity of monolingual and bilingual 4.5-month-old infants using EEG, while listening to their native/dominant language and two foreign languages. We defined two different windows of analysis to separate discrimination and identification effects. In the early window of analysis (150-280 ms) we measured the P200 component, and in the later window of analysis we measured Theta (400-1800 ms) and Gamma (300-2800 ms) oscillations. The results point in the direction of different language discrimination strategies for bilingual and monolingual infants. While only monolingual infants show early discrimination of their native language based on familiarity, bilinguals perform a later processing which is compatible with an increase in attention to the speech signal. This is the earliest evidence found for brain specialization induced by bilingualism.

Transcranial Direct Current Stimulation Does Not Counteract Cognitive Fatigue, but Induces Sleepiness and an Inter-Hemispheric Shift in Brain Oxygenation.

Sustained cognitive demands may result in cognitive fatigue (CF), eventually leading to decreased behavioral performance and compromised brain resources. In the present study, we tested the hypothesis that transcranial direct current stimulation (tDCS) would counteract the behavioral and neurophysiological effects of CF. Twenty young healthy participants were tested in a within-subject counterbalanced order across two different days. Anodal tDCS (real vs. sham) was applied over the left prefrontal cortex. In the real tDCS condition, a current of 1.5 mA was delivered for 25 min. Cortical oxygenation changes were measured using functional Near Infrared Spectroscopy (fNIRS) on the frontal cortices. CF was triggered using the TloadDback task, a sustained working memory paradigm that allows tailoring task demands according to each individual's maximal cognitive capacity. Sustained cognitive load-related effects were assessed using pre- versus post-task subjective fatigue and sleepiness scales, evolution of performance accuracy within the task, indirect markers of dopaminergic activity (eye blinks), and cortical oxygenation changes (fNIRS) both during the task and pre- and post-task resting state periods. Results consistently disclosed significant CF-related effects on performance. Transcranial DCS was not effective to counteract the behavioral effects of CF. In the control (sham tDCS) condition, cerebral oxygen exchange (COE) levels significantly increased in the right hemisphere during the resting state immediately after the induction of CF, suggesting a depletion of brain resources. In contrast, tDCS combined with CF induction significantly shifted interhemispheric oxygenation balance during the post-training resting state. Additionally, increased self-reported sleepiness was associated with brain activity in the stimulated hemisphere after recovery from CF during the tDCS condition only, which might reflect a negative middle-term effect of tDCS application.

Automatic detection of noisy channels in fNIRS signal based on correlation analysis.

BACKGROUND: fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. METHODS: In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. RESULTS: The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. COMPARISON WITH EXISTING METHOD(S): Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). CONCLUSIONS: Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost.

EEG feature selection using mutual information and support vector machine: A comparative analysis.

The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features by feature selection methods based on forward-backward procedure and mutual information as relevance criteria. Results confirm that fractional Fourier transform coefficients present very good performance and also the possibility of using some combination of this features to improve the performance of the classifier. To reinforce the relevance of the study, we carry out 1000 independent runs using a bootstrap approach, and evaluate the statistical significance of the F(score) results using the Kruskal-Wallis test.

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions.

This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time-frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.

New approach in features extraction for EEG signal detection.

This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.