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

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

Integration of occupational risk prevention courses in engineering degrees: Delphi study

Engineering syllabi often lack courses covering occupational risk prevention. In Spain, professional competences are awarded along with the completion of a university degree. This means that new graduates are certified in areas in which they have received little or no training, such as occupational risk prevention. However, the academic reforms established by the Bologna Process, which strives to homogenize university degrees throughout Europe, compels European universities to design new syllabi. The main goal of this paper is to define a framework for including occupational risk-prevention education in the new engineering syllabi. This exploratory research applied the Delphi methodology to a panel of 59 experts, using questionnaires assessed with a four-point Likert scale through two rounds. A website supported the information flow. According to the experts who participated in this study, education and training in occupational risk-prevention is essential for improving the safety culture within a company or workplace. The experts concurred that this subject should be a separate mandatory course in all engineering degree programs. The participants recommended that an optional course should be considered only if a mandatory course is not approved. It was also deemed desirable to integrate occupational risk prevention as a cross-field subject in other technological courses, even if the curriculum already includes some related courses. © 2012 American Society of Civil Engineers.Cortés Díaz, JM.; Pellicer Armiñana, E.; Catalá Alís, J. (2012). Integration of occupational risk prevention courses in engineering degrees: Delphi study. Journal of Professional Issues in Engineering Education and Practice. 138(1):31-36. doi:10.1061/(ASCE)EI.1943-5541.0000076S3136138

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%

Metrological foundations of emotional valence measurement through an EEG-based system

A contribution regarding the measurability of emotions and a reflection on its main related issues, are provided. The proposed use case is an electroencephalography-based detection system of positive and negative valence states. Some metrological characteristics of the proposed system were considered, i.e. reproducibility, sensitivity, and resolution. The issues concerning the measurability of emotions as lack of experimental, cross-subject, and within-subject reproducibility, as well as uncertainty induced by the adopted stimuli, were highlighted. A theoretical reference model was first identified, namely the circumplex model of affect, which is based on an interval scale. A standardized stimuli set known as Oasis was exploited. Furthermore, an initial screening of the participants was carried out to manage the bias of depressive disorders, and a compatibility analysis was conducted between the experimental sample and the sample exploited by the standardized dataset. The effectiveness of the emotion induction was maximized by choosing a polarized subset of stimuli and an implicit-more controlled mood induction procedure. A Self-Assessment Manikin was employed to verify the effectiveness of the induction procedure. The validity of the proposed method was experimentally proved. EEG signals from 25 healthy subjects were acquired through a 8-channel device. As a result, an average accuracy of 96.1 % in the within-subject case and an average accuracy equal to 80.2 % in the cross-subject case, were obtained

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

Preliminary validation of a measurement system for emotion recognition

An highly-wearable (wireless, few-channels and dry electrodes) device is proposed for EEG based valence emotion recognition. The component is a part of an instrument for real time engagement assessment in rehabilitation 4.0. The frontal, central, and occipital asymmetry were used as well known features related to emotional valence. The device was metrologically characterized on human subjects emotionally elicited through passive viewing of pictures taken from Oasis data set. As metrological references, a standardized test, the Self Assessment Manikin, was exploited. A 2nd order polynomial kernel-based Support Vector Machine reached 83.2 ± 0.3% accuracy in classifying emotional valence from each 2-s epoch of EEG acquired signals

High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation

A method for high-wearable EEG-based assessment of pediatric emotional and cognitive engagement in neuro-motor rehabilitation is proposed. A specific easy calibration is provided in the perspective of a personalized medicine. Due to the lack of studies evaluating pediatric multidimensional engagement, an observational non-interventional protocol was adopted for collecting the EEG data related to the high/low levels of engagement. The experimental validation of the proposed method involved four children performing a rehabilitation exercise in five 8-min sessions. Due to the age and frailty of the subjects, no negative emotions were expressly induced and an unbalanced dataset was obtained. Different Synthetic Minority Oversampling Technique (SMOTE)-based strategies for unbalanced dataset management and classification methods were compared. The highest performances were achieved by combining Artificial Neural Network (ANN) models with the KMeansSMOTE oversampling method. Balanced accuracies of 71.2 % and 74.5 % for the emotional engagement and the cognitive engagement are obtained, respectively

Proteomic approach for the analysis of acrylamide-hemoglobin adducts Perspectives 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 alpha- and beta-globin chains isolated from hemolysate samples incubated in vitro with acrylamide at different molecular ratios allowed us to identify Cys93 of beta-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 Cys 104 of alpha-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