Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton.

Francisco J Badesa,Surjo R Soekadar,Nicola Vitiello,Nicolas Garcia-Aracil,Simona Crea,Emilio Trigili,Marius Nann,Francesca Cordella,Jorge A Diez,Jose Maria Catalan,Loredana Zollo

doi:10.3390/s19224931

Abstract

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

Highlights

Around 80 million people in the EU, a sixth of its population, have a disability
Some parameters were computed: (i) the performance using each interface; (ii) the activation time computed as the average time to trigger each movement with each interface; and (iii) features extracted from the analysis of the following physicological signals
The temporal analysis performed to the four physiological features processed, states a relationship between the amount of time exhaustively using the interfaces with the changes in the physiological response of the subjects

Summary

Introduction

Around 80 million people in the EU, a sixth of its population, have a disability. They are often hindered from full social and economic participation by various barriers related to physical, Sensors 2019, 19, 4931; doi:10.3390/s19224931 www.mdpi.com/journal/sensorsSensors 2019, 19, 4931 psychological and social factors. Around 80 million people in the EU, a sixth of its population, have a disability. They are often hindered from full social and economic participation by various barriers related to physical, Sensors 2019, 19, 4931; doi:10.3390/s19224931 www.mdpi.com/journal/sensors. The percentage of people with disabilities is set to rise as the EU population ages [1]. Using brain-machine interfaces (BMIs) or brain-computer interfaces (BCIs) in combination with assistive robotic devices, such as wearable robots, has the potential to augment the capabilities of disabled people to carry out activities of daily living with success. Recent developments of BMIs or BCIs allow detection and translation of electric, magnetic or metabolic activity into control signals of external devices or machines. They were introduced for the first time by [2]

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