Development of an EEG Headband for Stress Measurement on Driving Simulators

Abstract

In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.

Would You Trust Driverless Service? Formation of Pedestrian’s Trust and Attitude Using Non-Verbal Social Cues

“…Near conversational distance refers to the vehicle actively approaching within the personal distance of the pedestrian for conversation, while far refers to the vehicle carefully keeping its distance within the boundary of social zones for conversation. Meanwhile, following previous studies [ 42 , 43 , 44 , 45 ], eye movement in our study means eyes blinking and gazing. This variable is classified as yes or no.…”

“…Additional details on the manipulation of non-verbal social cues are provided in Table 2 (4th and 5th column). All the stimuli were created for the VR environment to simulate the reality of the scenarios [ 42 , 61 ]. These stimuli contents can be fonund in Supplementary Materials .…”

“…Eye movement, a sub-element of kinesics suggested by Feine et al [ 39 ], has been examined by several scholars in human–robot interaction (HRI) highlighting the importance of the interaction effect between proxemics (i.e., the physical distance between human and robot) and gaze [ 41 , 42 , 43 , 44 , 45 ]. In addition, AV’s eyes are the design element that can effectively interact with pedestrians through minimal movement in situations where there is no other part of the AV’s body that can move freely.…”

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals

“…In this section, we describe the architecture of the sensors used in the experiments introduced above. In the first experiment, we used our own EEG headband design, which is characterized in [ 32 ]. To briefly describe its characteristics and specifications, the headband has six electrodes which acquire EEG signals from the scalp; two of them are located in the frontal region, two in the central region, and two in the occipital region.…”

Long-Term Exercise Assistance: Group and One-on-One Interactions between a Social Robot and Seniors

“…In the future, this limitation can be addressed by using a network of intermediary devices that connect to the sensors via Bluetooth and connect to a central host computer through a local network. Other solutions include the use of sensors with communication technology that reduces the restrictions on the number of connected devices (e.g., Wi-Fi-enabled EEG-sensors [105]). Other forms of user state estimation that do not require wireless communication can also be considered.…”