EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

Comparing online cognitive load on mobile versus PC-based devices.

Navigating the web represents a complex cognitive activity that requires effective integration of different stimuli and the correct functioning of numerous cognitive abilities (including attention, perception, and working memory). Despite the potential relevance of the topic, numerous limitations are present throughout the literature about the cognitive load during online activities. The main aim of this study is to investigate cognitive load during comprehension and information-seeking tasks. In particular, we here focus on the comparison of the cognitive load required while performing those tasks using mobile or PC-based devices. This topic has become even more crucial due to the massive adoption of smart working and distance learning during the COVID-19 pandemic. A great effort is nowadays devoted to the detection and quantification of stressful states induced by working and learning activities. Continuous stress and excessive cognitive load are two of the main causes of mental and physical illnesses such as depression or anxiety. Cognitive load was measured through electroencephalography (EEG), acquired via a low-cost wireless EEG headset. Two different tasks were considered: reading comprehension (CO) of online text and online information-seeking (IS). Moreover, two experimental conditions were compared, administering the two tasks using mobile (MB) and desktop (PC) devices. Eleven participants were involved in each experimental condition, MB and PC, performing both the tasks on the same device, for a total of twenty-two people, recruited from students, researchers, and employees of the university. The following two research questions were investigated: Q1: Is there a difference in the cognitive load while performing the comprehension and the information-seeking tasks? Q2: Does the adopted device influence the cognitive load? The results obtained show that the baseline (BL) requires the lower cognitive load in both the conditions, while in IS task, the requirement reaches its highest value, especially using a mobile phone. In general, the power of all the brain wave bands increased in all conditions (MB and PC) during the two tasks (CO and IS), except for alpha, which is usually high in a state of relaxation and low cognitive load. People include website navigation into their daily routines, and for this, it is important to create an interaction that is as easy and barrier-free as possible. An effective design allows a user to focus on interesting information: many website architectures, instead, are an obstacle to be overcome; they impose a high cognitive load and poor user experience. All these aspects draw cognitive resources away from the user's primary task of finding and comprehending the site's information. Having information about how the cognitive load varies based on the device adopted and the considered task can provide useful indicators in this direction. This work suggests that using an EEG low-cost wearable device could be useful to quantify the cognitive load induced, allowing the development of new experiments to analyse these dependencies deeper, and to provide suggestions for better interaction with the web.

Databases for evaluating interferences between affective content and image quality.

The two databases here described were generated to evaluate the role of affective content while assessing image quality (Corchs et al., 2018) [1]. The databases are composed of images JPEG-compressed together with the subjective quality scores collected during psychophysical experiments. To reduce interferences in quality perception due to image semantic, we have restricted the semantic content, choosing only close-ups of face images, and we have considered only two emotion categories (happy and sad). We have selected 23 images with happy faces and 23 images with sad faces of high quality. For what concerns image quality we have considered JPEG-distortion with 4 levels of compression, corresponding to q-factors 10, 15, 20, 30. The first image database, hereafter called MMSP-FaceA, is thus composed of 230 images (23+23) × 5 quality levels (including the original high quality pristine images). To better consider only interferences in quality perception due to affective content, we have generated a second image database where the background of images belonging to MMSP-FaceA has been cut off. This second image database is labelled as MMSP-FaceB. Psychophysical experiments were conducted, on a controlled web-based interface, where participants rated the image quality of the two databases in a five point scale. The two final databases MMSP-FaceA and MMSP-FaceB are thus composed of 230 images each, together with the raw quality scores assigned by the observers, and are available at our laboratory web site: www.mmsp.unimib.it/download.

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness.

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

Corrigendum: Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness.

[This corrects the article DOI: 10.3389/fnins.2019.00807.].

Clustering the Brain With "CluB": A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data.

In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called "cluster composition analysis," permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a "Gold Standard" of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast "reading > baseline") were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the "Gold Standard" to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC1 = 0.93). These results suggested that methods based on hierarchical clustering (and post-hoc statistics) and methods requiring prior Gaussian transformation of the data can be used as complementary tools, with the GingerALE method being optimal for neurofunctional mapping of pooled data according to simpler designs, and the CluB method being preferable to test more specific, and localized, neurocognitive hypotheses according to factorial designs.

Predicting Complexity Perception of Real World Images.

The aim of this work is to predict the complexity perception of real world images. We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images.

Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data.

A computational neuroscience framework is proposed to better understand the role and the neuronal correlate of spatial attention modulation in visual perception. The model consists of several interconnected modules that can be related to the different areas of the dorsal and ventral paths of the visual cortex. Competitive neural interactions are implemented at both microscopic and interareal levels, according to the biased competition hypothesis. This hypothesis has been experimentally confirmed in studies in humans using functional magnetic resonance imaging (fMRI) techniques and also in single-cell recording studies in monkeys. Within this neuro-dynamical approach, numerical simulations are carried out that describe both the fMRI and the electrophysiological data. The proposed model draws together data of different spatial and temporal resolution, as are the above-mentioned imaging and single-cell results.

Feature-based attention in human visual cortex: simulation of fMRI data.

We use a computational neuroscience approach to study the role of feature-based attention in visual perception. This model is used to numerically simulate a visual attention experiment. The neurodynamical system consists of many interconnected modules that can be related to the dorsal and ventral paths of the visual cortex. The biased competition hypothesis is taken into account within the model. From the experimental point of view, measurements exist, which confirm that feature-based attention influences visual cortical responses to stimuli outside the attended location. These measurements show that attention to a given stimulus attribute (in this case "color red") increases the response of cortical visual areas to a spatially distant, ignored stimulus that shares the same attribute. Our neurodynamical model is used to numerically compute the neural activity of area V4 corresponding to such ignored stimulus, giving a good description of the experimental data.