A Wearable Brain-Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain-computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values

Wearable Brain-Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

An instrument for remote control of the robot by wearable brain-computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device's wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance

High-wearable EEG-based distraction detection in motor rehabilitation

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness

Metrological performance of a single-channel brain-computer interface based on motor imagery

In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset '2a' of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG

Robotic Autism Rehabilitation by Wearable Brain-Computer Interface and Augmented Reality

An instrument based on the integration of Brain Computer Interface (BCI) and Augmented Reality (AR) is proposed for robotic autism rehabilitation. Flickering stimuli at fixed frequencies appear on the display of Augmented Reality (AR) glasses. When the user focuses on one of the stimuli a Steady State Visual Evoked Potentials (SSVEP) occurs on his occipital region. A single-channel electroencephalographic Brain Computer Interface detects the elicited SSVEP and sends the corresponding commands to a mobile robot. The device's high wearability (single channel and dry electrodes), and the trainingless usability are fundamental for the acceptance by Autism Spectrum Disorder (ASD) children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on 10 healthy adult subjects highlighted an average accuracy higher than 83%. Preliminary further tests at the Department of Translational Medical Sciences of University of Naples Federico II on 3 ASD patients between 8 and 10 years old provided positive feedback on device acceptance and attentional performance

EEG-based detection of emotional valence towards a reproducible measurement of emotions

A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the suggested use case. Valence detection occurs along the interval scale theorized by the Circumplex Model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. EEG signals were acquired through a 8-channel dry electrode cap. An implicit-more controlled EEG paradigm was employed to elicit emotional valence through the passive view of standardized visual stimuli (i.e., Oasis dataset) in 25 volunteers without depressive disorders. Results from the Self Assessment Manikin questionnaire confirmed the compatibility of the experimental sample with that of Oasis. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e., Hemispheric Asymmetry Theories), and (ii) automated (i.e., a pipeline of a custom 12-band Filter Bank and Common Spatial Pattern). An average within-subject accuracy of 96.1 %, was obtained by a shallow Artificial Neural Network, while k-Nearest Neighbors allowed to obtain a cross-subject accuracy equal to 80.2%

Measuring the drug absorbed by biological tissues in laboratory emulation of dermatological topical treatments

An experimental procedure for measuring the drug absorbed by a biological tissue in laboratory emulation of dermatological topical treatments is proposed. Laboratory emulation is based on the analysis of the eggplant electrical reaction to the injection of drug. Eggplant and human tissue are both well modeled by a distributed circuit model described by the ColeCole empirical equation. An exploratory measurement campaign aimed at investigating the relationship between the injected drug amount and the measured impedance is reported. The basic ideas, the measurement system design, and the proposed measurement procedure are illustrated. Then, its feasibility is proved experimentally and the results of the metrological characterization are reported and discussed. Results point out that, by a simple measurement of the impedance module (and not a spectroscopy), the amount of injected drug can be assessed by acceptable uncertainty

A Single-Channel SSVEP-Based Instrument with Off-The-Shelf Components for Trainingless Brain-Computer Interfaces

A high wearable instrument for brain-computer interface (BCI), based on steady-state visual evoked potentials, and conceived with low-cost, off-The-shelf components, is proposed. Peculiar features are: 1) a single-channel differential acquisition; 2) active transducers using dry electrodes with metallic pins; 3) real-Time computation based on Goertzel algorithm, lighter than fast Fourier transform; and 4) absence of training need before the first use. In this way, the proposed instrument overcomes the state-of-The art issues of comfort, wearability, signal quality, and feasibility on limited resources devices (e.g., tablets or smartphones) of BCI applications. The accuracy results of the instrument prototype, assessed in an experimental campaign on human subjects in laboratory, foster its application in wearable biomedical devices

An Ultrasonic Heading Goniometer Intrinsically Robust to Magnetic Interference

An ultrasonic heading measurement method, working under magnetic interference prohibitive for magnetometers, was prototyped, validated, and metrologically characterized. Two capacitive ultrasonic transducers convert the mechanical rotation in two correspondingly time-delayed electrical sine waves. Then, the time delay is estimated using the standard tree parameter sine-fitting algorithm. The prototyped goniometer achieves the same repeatability level (<36 mrad) of a magnetometer-based heading in the range [-437 mrad, 437 mrad]. Simultaneously, a throughput of 505 Sa/s is proven on an STM32F303xC Arm Cortex -M4 32-bit microcontroller. An interference analysis revealed the experimental deterministic error well explained by the combined effect of beacon and receiver directivity, as well as by the relative position of beacon, receiver, and reflective surfaces. Noise robustness was assessed in case of SNR decayed to 9.1 dB from the initial value of 36.6 dB; the maximum deterministic error in the range [-437 mrad, 437 mrad] increased less than 10% (from 21 to 23 mrad in absolute value)

Proteomic approach for tha analysis of acrylamide-hemoglobin adducts Perspective for biological monitoring

The formation of adducts between acrylamide and hemoglobin in vitro was investigated by using mass spectrometric methodologies to identify the amino acid residues sensitive to alkylation. Liquid chromatography–electrospray ionisation mass spectrometry analysis of either intact or trypsin-digested *- and *-globin chains isolated from hemolysate samples incubated in vitro with acrylamide at different molecular ratios allowed us to identify Cys93 of *-globin as the most reactive site in hemoglobin, according to a Michael-type addition reaction between acrylamide and the sulphydryl group of cysteine. The only other reactive sites were Cys104 of *-globin and the N-terminal amino groups of both chains. The method developed, based on electrospray ionisation quadrupole time-of-flight tandem mass spectrometry analysis of intact globin chains was able to specifically detect low levels of adducts. In this way, rapid identification of alkylated portion of Hb was achieved to be potentially used as a biomarker for high-sensitivity biological monitorin