Objective. Processing strategies are analyzed with respect to the classification of electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor imagery (MI). A review of literature is carried out to understand the achievements in MI classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery-based BCIs. Article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85%–100% range for the binary case and in the 83%–93% range for multi-class one. Associated uncertainties are up to 6% while repeatability for a predetermined dataset is up to 8%. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analyzed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a BCI. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of the results reproducibility.

Objective. Recent trends in brain–computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG). Approach. Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects’ perceived engagement was quantified using a questionnaire. Main results. The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects’ perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r _rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Augmented Reality (AR) offers a new communication channel, capable of enhancing human-robot interactions. A Brain-Computer Interface (BCI) can be particularly effective to interact with an AR headset, essentially working as a “thinking-mouse” and allowing hands-free control of the system, so that the operator can simultaneously carry out other tasks. In this paper, the feasibility of employing smart glasses driven by Steady-State Visually Evoked Potentials (SSVEP) non-invasive single-channel BCI is assessed in order to enhance the human-robot interaction in the Industry 4.0 framework. In particular, we demonstrate that smart glasses can generate a stimulus signal enough stable in frequency and bright to evoke SSVEP in the BCI user's brain. For the experimental AR case study of this work, the smart glasses Epson Moverio BT-200 were employed.

In the frame of the Next Generation Europe program, the EU program to boost after-COVID recovery, the Italian Minister of University and Research has funded a project called Innovative Research Infrastructure for applied Superconductivity (IRIS). New laboratories will be built or upgraded in six poles: Milan (hub of the infrastructure), Genoa, Frascati, Naples, Salerno, and Lecce, to carry out basic research on magnetism and superconducting materials, test of wires, tapes, and large current cables, superconducting magnets construction with advanced instrumentation, power tests of magnets, and a special facility for high current-high voltage superconducting lines. The program will be executed over three years and then will operate for at least 10 years. It includes two first demonstrators: one HTS magnet to be operated at 10–20 K and a superconducting line of 1 GW (40 kA–25 kV) about 140 m long. The demonstrators anticipate the main scope of the investment in the IRIS infrastructure: to support the use of superconductivity for improving sustainability by decreasing the energy consumption without compromising performance. This article describes the global IRIS project.

This article proposes the characterization of stimulation and detection equipment in the design of wearable brain–computer interfaces (BCIs) based on visually evoked potentials. In particular, methods for the characterization of the optical output of commercial smart glasses and for the calibration of a low-cost electroencephalograph (EEG) are presented and discussed. The reported results show that, by proper characterization and calibration, consumer-grade equipment is suitable for BCI applications. Moreover, unexpected harmonics of the visual stimuli have to be taken into account by suitable processing. Finally, the EEG shows an adequate linear behavior, but its gain error of around 10% should be corrected by calibration in some applications.